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FULL LENGTH ARTICLE

Assessing climate change impacts on wheat

production (a case study)

J. Valizadeh a, S.M. Ziaei b, S.M. Mazloumzadeh b,*

a University of Sistan and Baluchestan, Faculty of Biology, Zahedan, Iranb University of Sistan and Baluchestan, Faculty of Agriculture and Natural Resource of Saravan, Saravan, Iran

Received 21 November 2012; accepted 18 February 2013

Available online 26 February 2013

KEYWORDS

Simulation;

Model;

Scenario;

Phenology;

Climatic parameters

Abstract Climate change is one of the major challenges facing humanity in the future and effect of

climate change has been detrimental to agricultural industry. The aim of this study was to simulate

the effects of climate change on the maturity period, leaf area index (LAI), biomass and grain yield

of wheat under future climate change for the Sistan and Baluchestan region in Iran. For this pur-

pose, two general circulation models HadCM3 and IPCM4 under three scenarios A1B, B1 and A2in three time periods 2020, 2050 and 2080 were used. LARS-WG model was used for simulating

climatic parameters for each period and CERES-Wheat model was used to simulate wheat growth.

The results of model evaluation showed that LARS-WG had appropriate prediction for climatic

parameters and simulation of stochastic growing season in future climate change conditions for

the studied region. Wheat growing season period in all scenarios of climate change was reduced

compared to the current situation. Possible reasons were the increase in temperature rate and the

accelerated growth stages of wheat. This reduction in B1 scenario was less than A1B and A2 sce-

narios. Maximum wheat LAI in all scenarios, except scenario A1B in 2050, is decreased compared

to the current situation. Yield and biological yield of wheat in both general circulation models

under all scenarios and all times were reduced in comparison with current conditions and the lowest

reduction was related to B1 scenario. In general, the results showed that wheat production in the

future will be affected by climate change and will decrease in the studied region. To reduce these

risks, the impact of climate change mitigation strategies and management systems for crop adapta-

tion to climate change conditions should be considered.ª 2013 King Saud University. Production and hosting by Elsevier B.V. All rights reserved.

1. Introduction

In the recent years, visible changes in temperature and rainfall

in both the global and regional aspects were known as climate

change phenomenon in terms of amount and time of occur-

rence and consequently have exerted different impacts on the

inputs and agricultural production (Wolf, 2002). Researchers

of the related sciences have disagreements in the area of causes

* Corresponding author. Tel.: +98 9153621963; fax: +98 54835242413.

E-mail address: [email protected] (S.M. Mazloumzadeh).

Peer review under responsibility of King Saud University.

Production and hosting by Elsevier

Journal of the Saudi Society of Agricultural Sciences (2014) 13, 107–115

King Saud University

Journal of the Saudi Society of Agricultural Sciences

www.ksu.edu.sawww.sciencedirect.com

1658-077X ª 2013 King Saud University. Production and hosting by Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.jssas.2013.02.002

mailto:[email protected]://dx.doi.org/10.1016/j.jssas.2013.02.002http://dx.doi.org/10.1016/j.jssas.2013.02.002http://dx.doi.org/10.1016/j.jssas.2013.02.002http://www.sciencedirect.com/science/journal/1658077Xhttp://dx.doi.org/10.1016/j.jssas.2013.02.002http://dx.doi.org/10.1016/j.jssas.2013.02.002http://www.sciencedirect.com/science/journal/1658077Xhttp://dx.doi.org/10.1016/j.jssas.2013.02.002mailto:[email protected]://crossmark.crossref.org/dialog/?doi=10.1016/j.jssas.2013.02.002&domain=pdf

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and nature of climate changes, but it seems that in the past dec-

ades the climate change impacts and especially its ecological

consequences have been so apparent that many of these con-

tradictions have been resolved. The main factor in increasing

the greenhouse effect, is the increase in the concentration of

gases CO2, CH4 and N2O and types of Halo-Carbons (like

CFC) due to the human activities; these gases have an essential

role in absorbing the solar radiation that the climate system is

also affected by this issue (Tubiello et al., 2000).Although the climate change in some areas of the world,

particularly the areas located within the northern widths

above 55, will have positive effects on agricultural produc-

tion (Ewert et al., 2005), but the negative impacts of these

changes will be so severe in hot and dry areas (Parry

et al., 2004; Gregory et al., 2005), so in developing countries

the rise in temperature and the decrease in rainfall have

been more severe (Sivakumar et al., 2005), and moreover

the frequency and intensity of the occurrence of rare climatic

phenomena (drought, heat, coldness and flood) will also be

intensified (IPCC, 2007). Undoubtedly, any change in

climatic condition will affect the agricultural production

systems of the world.

Although in recent years, experiments conducted in con-trolled environments, have provided a lot of information about

the impacts of rise in temperature degree and carbon dioxide

concentration on plants’ growth and development processes,

but these studies are very costly and their implementation de-

pends on the exact instruments (Koocheki et al., 2001). Devel-

opment of modeling techniques is a suitable and low-cost

substitute for these types of studies that has already been con-

sidered by researchers. General circulation model (GCM) is

the right and accurate tool for the prediction of future climatic

condition and provides necessary data to run simulation mod-

els of the crops’ growth and development under climate change

condition (Jones and Thornton, 2003). The work of these mod-

els is based on global warming and the impact of rise in green-house gas concentration and the climatic subsequent

consequences are predicted on the basis of the rise of land sur-

face temperature. Because the future climate change depends

on the intensity of global warming, predicting the future tem-

perature of the earth is of special importance (Morison, 2006).

Global circulation model is a mathematical estimation of the

natural climate systems which offer on atmospheric circulation

and energy exchange between the main components of the cli-

mate systems as a model.

Obtaining more precise information about the phenomenon

of climate change in Iran requires further studies on a regional

scale and the prediction of agricultural production systems’ re-

sponse to these changes in each region. Our country belongs to

the arid and semiarid areas which are more sensitive to envi-ronmental changes and more vulnerable due to their special

ecological structures. So it seems that the occurrence of prob-

able climatic changes in these regions has a significant impact

on agricultural production systems (Fisher et al., 1994). How-

ever despite the world’s most arid and semiarid areas have

been located in developing countries, studies and scientific re-

searches related to climate change impacts in these areas are

very limited. The results of studies related to climate changes

that have been done in recent years in Iran, all have confirmed

the occurrence of this phenomenon in the country. Koocheki

and Nassiri (2008) have studied the climate change impact

on the water yield of the country by using a growth simulation

model and on the basis of various scenarios of climate change

and reported that on average the wheat yield in the country

will be reduced in the range between 14% and 21%.

Prediction and premonition of how climate change acts and

its impact on agricultural department can help the human to

deal with these changes. By predicting the extent and intensity

of climate change, the crop production changes can be specifiedin different regions and it can also affect the sustainability of

agricultural production especially in arid and semiarid areas.

So the purpose of this study is to evaluate the performance of

CERES-wheat model and the simulation of the climate change

impacts on phonological stages, leaf area index (LAI), biomass

and grain yield of wheat plant in the studied region.

2. Material and methods

2.1. Study area

Sistan and Baluchestan region covers an area of about

178,502 km

2

. The province is located in the southeast of Iran,within 2503 and 31027 north latitude and and 631021 east

longitude.

2.2. Data set and climate model

The IPCC has defined standard greenhouse gas emission sce-

narios for use in the evaluation of projected climate change

based on various socioeconomic, technological and energy

use factors ( IPCC, 2007). The SRES-A2 scenario indicated

very heterogeneous world conditions with high population

growth rate, slight economic development and slow technolog-

ical change (Prudhomme et al., 2010). The SRES-B1 defines a

convergent world with a global population that peaks in mid-century and rapid changes in economic structures toward a

service and information economy (Wetterhall et al., 2009)

and the SRES-A1B scenario describes a world of rapid eco-

nomic growth, a global population that peaks in mid-century

and more efficient technologies based on a balanced energy

mix (Olesen et al., 2011). Two GCM models under three sce-

narios were used to project climate change effects on wheat

in this study. Daily climate data including, solar radiation,

maximum and minimum air temperature (C) and precipita-

tion (mm) were obtained for the period of 1975–2010 for stud-

ied region meteorological station. The GCM models include

the United Kingdom Met Office Hadley Center (HadCM3)

(Mitchell et al., 1995) and Institute Pierre Simon Laplace

(IPCM4) (Semenov and Stratonovitch, 2010) and scenarioswere SRES-A2, SRES-B1 and SRES-A1B.

In this study, LARS-WG was used to produce daily climat-

ically parameters as one stochastic growing season for each

projection period. This one year data included radiation, max-

imum and minimum air temperature and precipitation of loca-

tion for four projection times (1975–2010 (baseline), 2020,

2050 and 2080). LARS-WG is a stochastic weather generator

based on the series approach (Semenov and Stratonovitch,

2010). LARS-WG produces synthetic daily time series of solar

radiation, maximum and minimum temperature and precipita-

108 J. Valizadeh et al.

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tion. LARS-WG applies observed daily weather data for a gi-

ven site to compute a set of parameters for probability distri-

butions of weather variables as well as correlations between

them (Semenov and Brooks, 1999).

2.3. Field experiment

2.3.1. Crop model calibration

The Decision Support System for Agrotechnology Transfer(DSSAT) comprises six models for simulating the growth of

16 crops of economic importance (Jones and Thornton,

2003). The model has demonstrated high reliability under dif-

ferent climates, soil and management conditions (Quiring and

Legates, 2008). The CSM-CERES Wheat model is one of the

most popular and highly reliable wheat models and has been

evaluated in many sites across the world and the results indi-

cated its capability to simulate the development of roots and

shoots, growth and senescence of leaves and stems, biomass

accumulation and partitioning between roots and shoots, leaf

area index, root, stem, leaf, and grain growth under different

climatic conditions (Quiring and Legates, 2008).

CERES wheat was calibrated by an experiment which wasconducted in the year 1998 at the experiment station of the

College of Agriculture, Zabol University in the central part

of the studied region (Ferizeni, 1998). In this experiment the

effect of different amounts of irrigation on yield and yield

components of different varieties of wheat was assessed. Three

levels of irrigation as the main plots and five varieties of wheat

(Atrak, Sorkhtokhm, Cross Felat, Hirmand and Darab) as the

sub-plots by split plot experiment based on a randomized com-

plete block design with three replications were considered in

the experiment. The common planting date of wheat in the

study area is about early November. Some of the measured

factors such as, grain yield, biological yield, leaf area index

in different growth stages, plant height, 100 seed weight, days

to anthesis (DTA) and days to maturity and harvest indexwere provided for the model as observed data. CERES-Wheat

determines the yield using six genetic coefficients that differ by

variety. Accurate simulation of yield requires the correct genet-

ic coefficients. Genetic coefficients of the wheat varieties that

are used for simulating wheat yield are shown in Table 1.

We used the observed data to calibrate the model for project-

ing the DTA, LAI, grain and biological yield of wheat for Sis-

tan conditions.

2.3.2. Crop model validation

The field experiment was conducted in the year 2011 at the

experiment station of the College of Agriculture, Saravan

University. The data of this experiment were used for the

crop model validation. This experiment was carried out to

assess the effect of different amounts of nitrogen fertilizer

on yield and yield components of wheat. Treatments in-

cluded four levels of nitrogen fertilizer (0, 100, 200 and

300 kg ha1) which the experiment was conducted based on

a randomized complete block design with three replications.

Planting date of wheat was November 5 and the Cross felat

cultivar was planted. Grain and biological yield, HI, DTA

and LAI were measured and used as observed data to vali-

date the model.

2.4. Model validation

Several criteria were used to quantify the difference between

simulated and observed data. The root mean-squared error

(RMSE) was computed to measure the coincidence between

measured and simulated values (Eq. (1)) (Loague and Green,

1991), while mean deviation (RMD) was calculated to evaluate

systematic bias of the model (Eq. (2)). Model efficiency (ME)

was calculated to estimate model performance in relation to

the observed mean (Eq. (3)) (Nash and Sutcliffe, 1970). More-

over, linear regression was applied between simulations and

observations to evaluate model performance and correlation

coefficient (R2) for each simulation.

RMSE ¼ 100

O

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiP

n

i ¼1ðPi Oi Þ2

n

s ð1Þ

RMD ¼ 100

O

Xni ¼1

Pi Oi n

ð2Þ

ME ¼

Pni ¼1ðOi OÞ

2Pn

i ¼1ðPi Oi Þ2Pn

i ¼1ðOi OÞ2

ð3Þ

where P and O are simulated and observed data, respec-

tively, O is the mean of observed data and n is the number

of observations. The RMSE illustrated the model’s predic-

tion error by heavily weighting high errors, while the

RMD uses same weights for all errors, which tends to

smooth out discrepancies between simulated and observed

data. ME indicated the efficiency of the model and can havepositive or negative values (Bannayan and Hoogenboom,

2008; Huang et al., 2009).

Another criterion was the test of difference between simu-

lated and observed data which was 1:1 line. Under best

simulation the simulated and observed data should be the

same so its regression equation is y = x (1:1 line). Fitted

regression equation between simulated and observed data

(Simulated = a + b · Observed) with 1:1 line (Simu-

lated = Observed) was tested by t-test. Hypothesis 0 (H0) is

b = 1 and so hypothesis 1 (H1) is b „ 1 in the t-test. If H0 is

accepted so this means that the difference between simulated

and observed data is not significant.

Table 1 Cultivar coefficients used with the CSM-CERES-

Wheat model.

Cultivar PHINT G3 G2 G1 P5 P1D P1V

Atrak 85.5 1.8 3.4 1.9 4.6 3 4.4Sorkhtokhm 84.3 1.6 2.7 1.7 5.1 2.8 4.2Cross Felat 85 1.5 2.8 1.4 7.7 3.2 5.4Hirmand 86 2.2 3.2 1.5 7.1 3 4.6Darab 87 2.4 3.2 2.1 6.7 3 4.3

P1D = photoperiod sensitivity coefficient; P1V = Vernalization

sensitivity coefficient; P5 = thermal time from the onset of linear

filling to maturity; G1 = Kernel number per unit stem;

G2 = standard kernel size under optimum conditions (mg);

G3 = standard, non-stressed dry weight (total, including grain) of

a single tiller at maturity (g); PHINT = thermal time between the

appearance of leaf tips (degree days).

Assessing climate change impacts on wheat production (a case study) 109

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3. Results and discussion

An examination of the temperature changes in 25 years (1985–

2010) in the studied region showed that over time the annual

mean temperature has been increased (Fig. 1). In a way that

about 0.055 degree centigrade has been added to the mean

temperature of the region annually. These results confirm the

issue of climate change for this region.

3.1. Validation of the data resulted from LARS-WG

Validation results obtained from LARS-WG data showed that

LARS-WS has more accuracy in the simulation of maximum

temperature compared with the minimum temperature and

rainfall (Table 2). The maximum temperature simulated by

LARS-WG was estimated with the difference of ±%7.62 from

the observed data, as RMSE obtained for the maximum tem-

perature was ±%7.62 which shows the high accuracy of the

LARS-WG in the simulation of maximum temperature (Table

2). Considering that the amount of rainfall fluctuations was

high in different seasons in the studied region, it can be ex-

pected that LARS-WG has a lower accuracy in the simulation

of the amount of rainfall compared to the maximum and min-

imum temperature. As estimated RMSE for rainfall and min-

imum temperature was obtained ±%23.93 and ±%11.61

respectively, that shows the low accuracy of LARS-WG for

the simulation of the amount of rainfall for the studied region

(Table 2), in a way that the amount of rainfall was simulated

with the difference of %23.93 of the observed data and this dif-

ference was %11.61 for the minimum temperature (Table 2).

In general the validation results showed that the LARS-WG

has a good ability to simulate climatic data (rainfall, maximum

and minimum temperature) for the studied region and these

climatic data can be used for the stimulation of the probable

growth season in the future conditions of climate change and

Table 2 Comparison of simulated and observed climatic variables estimated with LARS-WG.

Month Precipitation (mm) Minimum temperature (C) Maximum temperature (C)

– Simulated Observed Simulated Observed Simulated Observed

January 5.90 3.40 4.00 2.30 19.00 19.40

February 41.70 44.90 5.10 6.40 23.70 20.20

March 25.40 22.00 9.20 10.60 29.00 27.60

April 6.00 4.00 17.00 16.00 28.10 26.40

May 0.40 0.00 21.00 19.90 38.00 35.40

June 1.20 0.00 20.40 22.90 35.10 37.60

July 0.00 0.00 25.00 26.60 42.50 39.70

August 0.00 0.00 27.00 24.80 37.50 41.20

September 1.90 0.00 20.90 20.00 35.90 37.90

October 0.50 0.00 14.50 16.30 35.10 32.90

November 0.00 0.00 6.20 8.80 28.00 26.80

December 23.10 26.40 3.70 2.30 18.70 20.30

RMSE (%) 23.93 11.61 7.62

Table 3 Comparison of simulated and observed studied traits

by Root Mean-squared Error (RMSE), Model Efficiency (ME)

and Root Mean Deviation (RMD).

Parameters RMD ME RMSE

Grain yield 1.36 0.98 7.86

Biological yield 1.41 0.96 9.59

Maximum LAI 1.69 0.71 10.02

Day to maturity 1.11 0.19 1.71

°

Figure 1 Air temperature trend for Zahedan for a 45-year period.


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the studies related to the impact of these climatic factors on the

growth and development of the crops.

3.2. Validation of the growth model

Due to the low values calculated for the RMSE, the difference

between predicated and observed data was low for all traits

that are indicative of the high ability of this model in the pre-

diction of these traits (Table 3). Predicted values for the traitsof biomass yield, leaf area index and the day to maturity, were

estimated respectively by the model with the difference of %

±9.59, % ±10.02 and % ±1.71 of the measured data (the

amounts of RMSE for the mentioned traits are respectively,

9.59, 10.02 and 1.71) which is the appropriate amount for

the validity of crop’s growth simulation model (Table 3).

Jamieson et al. (1998) reported that if the numeric value of

RMSE is in the range between 0 and 10, the model has a high

estimation of the observed data, if it is between 10 and 20, this

model has a good estimation, if it is between 20 and 30, the

estimation is average and if it is over 30, it has poor estimation.

Also a high correlation was observed between predicted and

measured values for the biomass yield, leaf area index and

the day to maturity with a correlation coefficient of 0.97,0.98 and 0.77 respectively, (Table 4).

The wheat yield was predicted by the model with the differ-

ence of 86.7% of the observed data (RMSE-7.86) and a high

correlation was obtained between simulated and observed

grain yield (correlation coefficient = 98.0)(Tables 3 and 4).

The RMD and ME values obtained for all the traits were

low that show the high accuracy of the model in the proper

evaluation of the simulated values for the mentioned traits

(Table 3).

The lowest correlation between the measured and simulated

data was related to the day to the flowering trait with a corre-

lation coefficient of 0/77 (Table 4). Considering the drown line

1:1 the difference between the simulated and observed data forall traits also shows the high accuracy of the model in the pre-

diction of these traits (Fig. 2). Test result for all the traits

showed that the zero supposition is acceptable for the slope

of fitted regression equation and this means that there is no sig-

nificant difference between the line 1:1 ( y = x) and the fitted

regression between observed and simulated data (Table 4

and Fig. 2). Model validation results for the day to maturity,

showed that the model has had a very good estimation of this

trait in comparison with other traits, though the correlation

between simulated and measured data for the trait of the day

to maturity was less (Table 4).

3.3. Climate change impact on wheat

3.3.1. The number of the days from planting to flowering

The simulated amount of the number of days from planting to

flowering of wheat in all scenarios and general circulation

models used for the three periods of 2020, 2050 and 2080

was less than the current situation of the studied region (Table

5). In other words the prediction results of the number of days

to flowering for the future of climate change showed that thistrait will decrease under the situation of climate change. Mean-

while the A2 scenario influenced by general circulation model

HadCM3 in the period of 2080 with 180 days, had the maxi-

mum rate of reduction in the number of days to flowering

within the current situation of climate change (Table 5). Also

the B1 scenario in the period of 2020 under the influence of

both general circulation models, revealed the least difference

within the present condition of this trait in Sistan by having

200 days from planting to flowering (Table 5). Lal et al.

(1998) reported that 1 rise in temperature leads to a reduction

of about 5 days in the flowering time of the wheat, affects the

time period of wheat flowering and reduces the time of grain

filling and as a result wheat yield in India.The simulation results of the number of days from plant-

ing to flowering of wheat in terms of climate change in the

studied region showed that from the time 2020–2080, the

reduction rate of this trait was more than the current situa-

tion (Table 5). As stated, it is anticipated that the tempera-

ture degree will be risen overtime that leads to the increase

in evaporation and transpiration, so it seems that the rise

in temperature, evaporation and transpiration, leads to the

increase in the growth rate of the plant and in other words

the plant efforts to complete the growth process by recogniz-

ing the heat stress condition and as a result the period of

planting to flowering is reduced. Also Roberts and Summer-

field (2007) stated that the rise in temperature during the

growth season leads to the fast growth and earlier floweringof the plant. Magrin et al. (1997) used the IS92 scenario

and observed that phonological stages from the emergence

to flowering and from flowering to physiological maturity

were reduced from about 10–15 days, respectively. They re-

ported that the biomass accumulation and grain filling were

affected by the negative impact of this factor and for the

other scenario, the time required to the completion of matu-

rity period was reduced for about 21–32 days. With the sim-

ulation of the climate change impact on the flowering time,

Ozdogan (2011) showed that this trait in three scenarios of

A2, A1B and B1 revealed about 9%, 13% and 6% decrease

respectively, according to the current situation of Turkey and

also reported that the amount of this reduction in the period

of 2061–2080 was more than the period of 2041–2060 and the

latter was more than the period of 2021–2040 and stated that

with the passage of time the amount of reduction in the flow-

ering time was decreased in the present condition of this

country.

The decrease in the growth period of barley (Jones and

Thornton, 2003), rice (Prasad et al., 2006; Mall and Aggarwal,

2002) and wheat (Lawlor and Mitchell, 2000; Remy et al.,

2003; Koocheki and Nassiri, 2008) was also reported in the

studies of climate change. Available evidence shows that the

decrease in the grain filling period due to the rise in tempera-

Table 4 The t-test for comparing the 1:1 line and the fitted

regression equation between observed and simulated data

( y = a + bx).

Parameters b t R2

Value Std. error tb

Grain yield 1.14 1.05 0.98 1.23

Biological yield 1.13 1.04 0.97 0.19

Maximum LAI 0.99 1.45 0.98 0.96Day to maturity 0.82 1.44 0.77 1.67


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Table 5 Simulated phenological stages, maximum leaf area index, biomass and grain yield (kg ha1) of wheat based on HadCM3 and

IPCM4 under A1B, A2 and B1 scenarios in 3 periods for Sistan and baluchestan province.

Period Scenario GCM Model Anthesis day Maturity Grain Yield kg ha1 Maximum LAI HI

Current A1B 201 226 4694.8 4.38 52

2020 197 217 2933.7 3.97 43

2050 193 217 4193.8 4.58 46

2080 192 213 3234.0 3.23 50

2020 A2 197 221 4266.9 4.04 48

2050 HadCM3 192 217 4224.0 3.52 47

2080 189 210 2961.2 3.26 45

2020 B1 200 224 4645.9 4.19 51

2050 196 218 3856.1 4.09 49

2080 192 215 3823.1 4.02 47

2020 A1B 196 220 4206.4 4.02 49

2050 195 219 4059.6 3.59 48

2080 190 211 3961.7 3.59 45

2020 A2 197 221 4197.1 3.92 47

2050 IPCM4 195 220 4143.2 3.56 50

2080 192 213 3111.9 3.16 47

2020 B1 200 225 4546.9 4.26 51

2050 197 220 4307.6 4.09 50

2080 194 216 3874.2 4.02 49

S i m u l a t e d

1:1 line

(y = x)

Grain Yield (kg ha-1

)

S i m u l a t e d

5000

6000

7000

8000

9000

(y = x)

1:1 line

Biological Yield (kg ha-1

)

Observed

2500 3000 3500 4000 4500 5000

2500

3000

3500

4000

4500

5000

5000 6000 7000 8000 9000

3.4

3.6

3.8

4.0

4.2

4.4

4.6

1:1 line

(y = x)

LAI

3.4 3.6 3.8 4.0 4.2 4.4 4.6 220 222 224 226 228 230

220

222

224

226

228

230

Day to maturity

(y = x)

1:1 line

Figure 2 Comparison of simulated and observed grain yield, biological yield, LAI and maturity day across data varying plant.


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ture is the main factor in the yield decrease of the crops in the

climate change condition (Challinor et al., 2007).

3.3.2. The number of the days from planting to maturity

The number of the days from planting to maturity also obeyed

the similar results of the number of the days from planting to

flowering of wheat (Table 5). As in all scenarios and general

circulation models used in all studied periods, the rate pre-

dicted by this trait is less than its present condition in the stud-

ied region and this difference increased over time. Among the

studied scenarios, the B1 scenario for both general circulation

models and in the period of 2020 showed the least reduction of

this trait within the present condition of the number of days of

planting to maturity of wheat and in turn the A2 scenario af-

fected by general circulation model HadCM3 and A1B sce-

nario affected by general circulation model IPCM4 for the

period of 2008 showed the greatest difference (Table 5). Thisreflects the impact of temperature rise based on scenarios

and general circulation model on the increase in the develop-

ment rate (Tao et al., 2006) and thus reduces the wheat growth

season. Though it has been reported that in the areas where the

crop’s growth season encounters limitation, climate change

and earth warming can lead to the improvement of crops’ yield

by increasing the growth season period and the improvement

of the plant flowering strength (Challinor et al., 2007).

The rise in temperature will increase the growth and devel-

opment speed of the crops, though experimental evidence has

showed that under this condition, the length of maturity of the

seed in the grains and seedy plants will be reduced (Parry et al.,

2004). Since achieving optimal performance depends on the so-

lid material accumulation during the growth season on one

hand and also on the existence of enough time to transfer

the material to the grain on the other hand, the rise in temper-

ature leads to the shorter length period of grain filling in the

grains and thus will decrease the performance of these prod-

ucts (Menzel, 2003).

3.3.3. The grain yield

The prediction results of wheat yield showed that this trait will

decrease under the influence of future climate changes in all

scenarios and general circulation models within the present

condition of the studied region (Table 5). It means that the pre-

dicted climate changes in Mashhad region will have a negative

impact on the wheat yield. The reduction rate of wheat yield

was variable between 1% and 37% (Fig. 3). Among all the sce-

narios and general circulation models used, in the time periodof 2020, under the general circulation model HadCM3, the

A1B scenario showed the maximum reduction of grain yield

compared to the wheat yield and after it the A2 scenario in

both general circulation models and A1B in general circulation

model HadCM3 had the maximum reduction of this trait in

the time period of 2080 (Table 5). On the other hand, the B1

scenario in the general circulation models HadCM3 and

IPCM4 with about 4546 kg of wheat yield, showed the least

reduction of this trait within the current situation of the stud-

ied region (Table 5) in a way that the yield reduction rate in

both general circulation models for wheat was about 1% and

4% within the current situation (Fig. 3). Also in a study in

the northwest of India, Lal et al. (1998) found that 4 degree

centigrade rise in the average daily temperature led to 54%

reduction of wheat yield in this region. Moreover with the

examination of climatic change impacts including rise in tem-

perature and carbon dioxide concentration on the wheat yield,

Mitchell et al. (1995) observed that the plant yield showed

reduction between 16% and 35% under the impact of temper-

ature rise within the current situation. Also it was observed in

Philippines that the rice yield was reduced about 10% with the

average daily temperature of 1 rise in temperature (Peng et al.,

2004). In all scenarios and general circulation models used, it

was observed that with the increase in time period from 2020

to 2080, the simulated yield rate was reduced (Table 5). For in-

stance in A2 scenario in general circulation model HadCM3,

the increase in time period from 2020 to 2080, the variabilityof wheat yield was from 10% to 36% compared to its yieldin the current situation of the studied region ( Fig. 3). The re-

sults of these studies were corresponding to Ozdogan’s

(2011), in a way that with the examination of various scenarios

in different time periods for different general circulation mod-

els (GFDL CM2.1, CSIRO MK3.5, HADCM3 and NCAR

CCSM 3.0), he also reported that the B1 scenario had less

reduction in wheat yield compared with A2 and A1B scenarios

within the current situation of Turkey and on the other hand

with the passage of periods studied from 2020 to 2080, the

reduction rate of wheat yield was more in Turkey in all scenar-

ios and general circulation models. He recognized that the

-40

-35

-30

-25

-20

-15

-10

-5

0 2 0 2 0

2 0 5 0

2 0 8 0

2 0 2 0

2 0 5 0

2 0 8 0

2 0 2 0

2 0 5 0

2 0 8 0

2 0 2 0

2 0 5 0

2 0 8 0

2 0 2 0

2 0 5 0

2 0 8 0

2 0 2 0

2 0 5 0

2 0 8 0

A1B A2 B1 A1B A2 B1

4MCPI3MCdaH

C h a n g e ( % )

Figure 3 Changing wheat grain yield as affected by climate change using HadCM3 and IPCM4 models under A1B, A2 and B1 scenarios

in Sistan & Baluchestan for 3 periods.


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reduction yield of wheat in simulated condition of climate

change is correspondent with the change in the rate of evapo-

ration and transpiration in wheat growth condition in this

country. By the examination of global warming impact on

the wheat production in Finland, Saarikko and Carter (1996)

also stated that the wheat yield was reduced in southern areas

of this country.

3.3.4. Harvest indexThe simulation results showed that the changes in this trait

obeyed the wheat yield. As the maximum rate of harvest index

in all scenarios and general circulation models was less than

the current situation of the studied region (Table 5). For the

time period of 2080, the A1B scenario under the impact of gen-

eral circulation model HadCM3 and the A2 scenario for the

general circulation model IPCM4, showed the maximum

change according to the rate of this trait in the current climatic

condition of Mashhad (52). On the other hand for the period

of 2080, the B1 scenario showed the least reduction of this trait

in both general circulation models within the current situation

of the studied region with 51% of harvest index. The reduction

of wheat harvest index in the climatic change condition of Sis-

tan in relation to its current situation, can illustrate this issue

that the climatic changes ahead, which have more emphasis

on the temperature rise on the basis of the mentioned scenarios

between 1.5 and 5 degree centigrade in the next 100 years

(IPCC, 2007), have less influenced on wheat biomass produc-

tion than the grain yield. It appears that the rise in temperature

in climate change condition has had negative impacts on the

grain filling period by reducing the plant’s growth period (Ta-

ble 5), and resulted in the reduction of wheat yield within its

current situation. Change in temperature and rainfall level, af-

fects the plant photosynthesis, growth and absorption rate and

water and nutrient distribution and as a result the leaf area in-

dex (Long, 1991).

3.3.5. The maximum leaf area index

The maximum level of simulated leaf area index in wheat plant

was variable between 3.16 and 4.38 (Table 5). The results

showed that the amount of this trait in all scenarios and gen-

eral circulation models of climatic change for all studied peri-

ods was less than the simulated amount within the current

situation of the studied region. It means that the maximum leaf

area index in the future climate change condition was reduced

according to the current situation of this trait in the studied re-

gion. Among the scenarios and general circulation models

used, for the time period of 2020, in both general circulation

models, the B1 scenario with about 2% difference, showed

the least reduction within the current situation of this trait ( Ta-ble 5). In general the rate of maximum leaf area index, for all

studied periods for B1 scenario was about four in both general

circulation models, and compared with other scenarios showed

less difference with simulated amount of current situation. In

all scenarios, the negative impacts of climate change on the

rate of maximum leaf area index of wheat was more over time,

in a way that the rate of this trait in all scenarios and in both

general circulation models in the period of 2020 was more than

2050, and in the latter was more than 2080 (Table 5).

The leaf area index indicates the canopy development and

thus the crop’s ability to absorb the radiation (Koocheki and

Kamali, 2010). So the leaf area reduction shows less absorp-

tion of light and consequently the lower rate of canopy photo-

synthesis. It seems that in the future climatic condition, lack of

water, will be the main factor in the reduction of leaf area

development (Koocheki and Kamali, 2010). The researches’

results (Van Ittersum et al., 2003; Kimball et al., 1995) have

shown that the soil moisture reduction in climate change con-

dition, limits the root availability to water and in this way will

reduce the wheat leaf area. On the other hand the temperaturerise in climate change leads to the fast accumulation of wheat

growth day’s degree and as a result with the increase in canopy

development rapidity, the maximum reaching of leaf area in-

dex will accelerate compared with the current situation ( Kirby,

1990). This issue will change the temporal coincidence between

the maximum solar radiation efficiency and the canopy devel-

opment and as a result the canopy efficiency decreases consid-

erably to absorb the radiation.

4. Conclusion

In this study the effects of climate change on the maturity per-

iod, leaf area index(LAI), biomass and grain yield of wheat un-

der future climate change for the studied region in Iran were

simulated. For this purpose, two general circulation models

HadCM3 and IPCM4 under three scenarios A1B, B1 and A2

in three time periods 2020, 2050 and 2080 were used. The re-

sults of model evaluation showed that LARS-WG had appro-

priate prediction for climatic parameters and simulation of

stochastic growing season in future climate change conditions

for the studied region. Wheat growing season period in all sce-

narios of climate change was reduced compared to the current

situation. Possible reasons were the increase in temperature

rate and the accelerated growth stages of wheat. This reduction

in B1 scenario was less than A1B and A2 scenarios. Maximum

wheat (LAI) in all scenarios, except scenario A1B in 2050, isdecreased compared to the current situation. Yield and biolog-

ical yield of wheat in both general circulation models under all

scenarios and all times were reduced in comparison with cur-

rent conditions and the lowest reduction was related to B1 sce-

nario. In general, the results showed that wheat production in

the future will be affected by climate change and will decrease

in the studied region. To reduce these risks, the impact of cli-

mate change mitigation strategies and management systems for

crop adaptation to climate change conditions should be

considered.

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