Combating the Harm Effect of Climate Change on Wheat Using Irrigation Water Management

Egypt J. Agric. Rec., 90 (4) , 2012

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Combating the Harm Effect of Climate Change on Wheat Using Irrigation

Water Management

Samiha Ouda1, M. A. A. Abdrabbo and Tahany Nor El din

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1 Water Requirements and Field Irrigation Research Department; Soils, Water and Environment

Research Institute; Agricultural Research Center; Egypt 2 Central Laboratory for Agricultural Climate; Agricultural Research Center; Egypt

3 Crop Physiology Research Department; Field Crops Research Institute; Agricultural Research

Center; Egypt

Abstract

The effect of climate change on wheat grown under sprinkler irrigation was

studied using previous data of two growing seasons, i.e. 2008/09 and 2009/10. These

data was used to calibrate CropSyst model. Furthermore, a field experiment

conducted at El-Bosaily farm El-Behira Governorate in 2010/11 growing season. The

data of this experiment was used to validate the CropSyst model. The treatments of

the experiment validation experiment composed of two wheat cultivars (Sakha 94 and

Sakha 93) and four irrigation treatments (0.6, 0.8, 1.0 and 1.2 of ETc). Four climate

change scenarios (A1, A2, B1 and B2) were used to assess the consequences of

climate change on wheat yield in four time slices (2040, 2060, 2080 and 2100). A new

irrigation schedule developed by BIS model was used to save irrigation water and

improve water productivity under climate change conditions. The results showed that

CropSyst model was able to predict wheat yield with high degree of accuracy for both

calibration and validation procedures. The results also indicated that, in general, the

yield of both cultivars will be decrease under climate change; however the reduction

was lower for Sakha 93, compared by Sakha 94. The lowest reduction in the yield of

both cultivars occurred in 2040 under B2 climate change scenario; whereas the

highest yield losses were obtained in 2100 under A2 climate change scenario. The

application of the new irrigation schedule under climate change conditions saved

irrigation water by 6, 27 and 39% under irrigation with 0.8, 1.0 and 1.2 of ETc,

respectively. Furthermore, it increased water productivity under the four climate

change scenarios, compared with irrigation amount resulted from 0.8, 1.0 and 1.2 of

ETc, for both wheat cultivars. Moreover, Sakha 93 gave the highest water

productivity. Our results suggested that if we want to reduce yield losses for wheat

under climate change conditions and save significant amounts of irrigation water,

Sakha 93 should be cultivated and BIS model should be used to schedule irrigation.

Key words: CropSyst model, climate change scenarios, BIS model, water

productivity.

Introduction

The Earth has warmed by 0.7˚C on average since 1900 (Jones and Moberg

2003). Most of the warming since 1950 is due to human activities that have increased


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greenhouse gases (IPCC 2001). There has been an increase in heat waves, fewer

frosts, warming of the lower atmosphere and upper ocean, retreat of glaciers and sea-

ice, an average rise in global sea-level of approximately 17 cm and increased heavy

rainfall in many regions (IPCC 2001 and Alexander et al. 2006). Many species of

plants and animals have changed their location or behavior in ways that provide

further evidence of global warming (Hughes et al., 2003).

To estimate future climate change, scientists have developed greenhouse gas

and aerosol emission scenarios for the 21st century. These are not predictions of what

will actually happen. They allow analysis of “what if?” questions based on various

assumptions about human behavior, economic growth and technological change

(Church and White 2006). Climate change as projected by climate models has the

potential to significantly alter the conditions for crop production, with important

implications for worldwide food security (Rosenzweig and Hillel 1998). Many studies

have documented the effects of climate change on wheat yield in Egypt and

concluded that the yield could be reduced by an average of 30% in the Nile Delta and

Valley under surface irrigation and old land (Eid et al., 1992; Eid et al., 1993 and Eid

et al., 1994, Khalil et al., 2009). Changes in yield behavior in relation to shifts in

climate can become critical for the economy of farmers. An increasing probability of

low returns as a consequence of the more frequent occurrence of adverse conditions

could prove dramatic for farmers operating at the limit of economic stress (Torriani et

al., 2007), especially for farmers cultivating low fertile soil. Under the projected

climate change, extra damage is expected to occur to the yield of cultivated crops in

these areas as a result of deterioration in the soil.

Crop simulation models can be used to assess the likely impact of climate

change on grain yield and yield variability. These crop models must accurately predict

several key characteristics over a wide range of climatic conditions, such as timing of

flowering and physiological maturity, through correct descriptions of phenological

responses to temperature and day length. Furthermore, accumulation of yield needs to

be predicted by accurately predicting the development and loss of leaf area and,

therefore, a crop's ability to intercept radiation, accumulate biomass, and partition it to

harvestable parts such as grain. Crop water use is also needed to be accurately

predicted by correctly predicting evapotranspiration and the extraction of soil water

by plants roots (Richter and Semenov 2005). CropSyst (Stockle et al., 1994) is one of


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these models that could be used along with a set of daily weather data spanning a

reasonable number of years to assess the impact of climate change on agriculture

(Tubiello et al., 2000; Torriani et al., 2007). The application of such models allows

the simulation of many possible climate change scenarios from only a few

experiments for calibration.

The objectives of this paper were: (i) to calibrate CropSyst model for wheat

grown in El-Behira governorates using previous field data; (ii) To validate CropSyst

model for field data experiment of wheat in the same governorate; (iii) to determine

yield losses under four climate change scenarios; (iv) to use BIS model to develop

new irrigation schedules under current climate and use it to run CropSyst model to

mange water more efficiently.

Materials and Methods

Previous data for wheat yield and consumptive use was obtained for 2008/09

and 2009/10 growing seasons. These data was used for calibrating the CropSyst

model. In addition, a field experiment was conducted in 2010/11 to collect the data

needed for validating the model.

1. The previous field data for calibration

Two field experiments were conducted at Aly Mobarak experimental farm of

the South Tahrir Research station, Egypt, during winter seasons of 2008/09 and

2009/10. Aly Mobarak experimental farm represents newly reclaimed sandy soil of

El-Bustan area at El-Behira governorate. Four irrigation treatments were used, i.e. I1:

Irrigation with 0.6 ETc, I2: Irrigation with 0.80 ETc, I3: Irrigation with 1.00 ETc and

I4: Irrigation with 1.20 ETc). The treatments were arranged in a randomized complete

block design with four replicates. Wheat cultivar Giza 168 was planted on 25th

of

November in the both growing seasons under sprinkler irrigation. All optimum

agricultural practices were preformed. Soil mechanical analysis according to (Piper,

1950) of the site of calibration experiment in the depth of the 0-60 cm is shown Table

(1)

Table (1) soil mechanical analysis of the soil of calibration experiment

Soil depth Sand Silt Clay Texture


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(cm) (%) (%) (%) Class

0 – 15

15 – 30

30 – 45

45 -60

91.00

90.50

90.30

90.20

3.70

3.80

3.90

4.00

5.30

5.70

5.80

5.80

Sandy

Sandy

Sandy

Sandy

Average 90.50 3.85 5.65

Chemical analysis of the experimental site is shown in Table (2). The soil

moisture constants (% per weight) and bulk density (g/cm³) in the depth of 0 – 60 cm

are shown in Table (3).

Table (2): Basic chemical analysis of the soil of calibration experiment

Soil depth

(cm)

Ec

(dS/m) PH Ca++

Soluble cations and anions (meq/L)

Mg Na K Co3 Hco3 Cl So4

0 – 30 0.35 9.13 1.23 0.54 1.56 0.17 - 1.10 1.73 0.67

30 – 60 0.30 9.38 1.25 0.49 1.61 0.15 - 1.07 1.74 0.69

Table (3): Soil water constants of the soil of calibration experiment

Soil depth

(cm)

Field capacity

( % ,w/w)

Wilting point

(% ,water)

Available

water (mm)

Bulk density

(g/cm³)

0 – 15

15 – 30

30 – 45

45 – 60

11.50

11.00

9.70

9.00

5.60

5.30

4.80

4.40

5.90

5.70

4.90

4.60

1.50

1.67

1.73

1.80

Average 10.30 5.00 5.30 1.68

Phenological data was collected in both growing seasons. Harvest was done on

2nd

week of April in both seasons. Wheat grain and biological yield were measured at

harvest and harvest index was calculated. All these measurements were used to

calibrate CropSyst model to make it capable of predicting the final wheat yield.

The field experiment for validation

A field experiment was conducted at El-Bosaily farm , Behira governorate in

2010/11 growing seasons for two wheat cultivars, i.e. Sakha 94 and Sakha 93. These

two cultivars were planted on the 24th

of November. Wheat was planted under

sprinkler irrigation in four irrigation treatments, i.e. irrigation with 0.6, 0.8, 1.0 and


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1.2 of ETc. All optimum agricultural practices were preformed Table (4) and (5)

showed the mechanical and chemical analysis of the experimental site.

Table (4): Mechanical analysis of the soil of the site of validation experiment.

Soil Depth Clay % Silt % Fine Sand % Coarse Sand %

10-30 18.4 40.6 29.5 11.5

30-60 17.5 42.4 30.9 9.2

60-90 17.4 40.6 30.4 11.6

Table (5): Chemical analysis of the soil of the site of validation experiment.

Depth

(cm) SP pH

ECe

(dS/m)

meq/l

Cations Anions

Ca++

Mg++

Na+

K+

Cl-

CO3--

HCO3-

SO4--

10-20 33 7.75 1.25 2.80 2.15 6.69 0.9 4.50 - 1.90 6.14

20-30 33 7.75 1.50 4.00 2.37 7.15 1.50 6.75 - 1.33 6.94

30-40 32 7.70 1.40 3.20 4.00 5.93 0.88 5.85 - 1.90 6.26

Maximum leaf area index (at anthesis) was measured. The date of

phenological stages was measured in the field. At harvest, grain and biological yield

were measured and harvest index was calculated.

2. CropSyst model

2.1. Model description

The Crop model (Stockle et al., 1994) objective is to serve as an analytical

tool to study the effect of cropping systems management on crop productivity and the

environment. For this purpose, CropSyst simulates the soil water budget, soil-plant

nitrogen budget, crop phenology, crop canopy and root growth, biomass production,

crop yield, residue production and decomposition, soil erosion by water, and pesticide

fate. These are affected by weather, soil characteristics, crop characteristics, and

cropping system management options including crop rotation, variety selection,


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irrigation, nitrogen fertilization, pesticide applications, soil and irrigation water

salinity, tillage operations, and residue management.

The water budget in the model includes rainfall, irrigation, runoff,

interception, water infiltration and redistribution in the soil profile, crop transpiration,

and evaporation. The nitrogen budget in CropSyst includes nitrogen application,

nitrogen transport, nitrogen transformations, ammonium absorption and crop nitrogen

uptake. The calculation of daily crop growth, expressed as biomass increase per unit

area, is based on a minimum of four limiting factors, namely light, temperature, water,

and nitrogen. Pala et al., (1996) suggested that minor adjustments of some of these

parameters, accounting for cultivar-specific differences, are desirable whenever

suitable experimental information is available. Details on the technical aspects and

use of the CropSyst model have been reported elsewhere (Stockle et al., 1994; Stockle

and Nelson 1994).

2.2. Model calibration

After each growing season, input files required by CropSyst model for El

Behira location and wheat crop were prepared and use to run the model. For each

treatment one management file was prepared represent each irrigation treatment. The

date of each phenological stage was used to calculate growing degree days for that

stage. Total biomass, grain yield, total and seasonal evapotranspiration, computed

from the soil-moisture measurements from all the treatments, were used for model

calibration. The values of the crop input parameters were either taken from the

CropSyst manual (Stockle and Nelson, 1994) or set to the values observed in the

experiments. The calibration consisted of slight adjustments of selected crop input

parameters to reflect reasonable simulations. These adjustments were around values

that were either typical for the crop species or known from previous experiences with

the model.

2.3. Model validation

The CropSyst model was validated using the field experiment data. It was

validated for grain and biological yield.


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2.4. Goodness of fit

To test the goodness of fit between the measured and predicted data, percent

difference between measured and predicted values of grain and biological yield in

each growing season were calculated. In addition, root mean square error (Jamieson et

al., 1998), which describes the average difference between measured and predicted

values, was calculated. Furthermore, Willmott index of agreement was calculated,

which take a value between 0.0-1.0 and 1.0 means perfect fit (Willmott, 1981).

2.5. Climate change scenarios

Four climate change scenarios were used to determine the effect of climate

change in four time slices, i.e. 2040, 2060, 2080 and 2100. Four climate change

scenarios were A1, A2, B1 and B2 (Wigley et al., 2000). The values in Table (6) were

developed for Egypt. A1 and B1 assumed homogeneous world, where A1 supposed

rapid economic growth and B1 assumed global environmental sustainability. A2 and

B2 supposed heterogeneous world, where A2 assumed regionally oriented economic

development and B2 assumed local environmental sustainability.

Table (6): Annual average increase in monthly mean temperature under four climate

change scenarios

Month 2040 2060 2080 2100

A1 1.7 2.3 2.8 3.1

A2 1.9 2.5 3.3 4.2

B1 1.3 1.8 2.2 2.4

B2 1.4 1.9 2.4 2.7

2.6. Irrigation water management under climate change

Irrigation was rescheduled using an irrigation scheduling model called BIS

(Snyder et al., 2004). The BIS model (The Basic Irrigation Scheduling) application

was written using MS Excel to help people plan irrigation management of crops. The

BIS program and a pdf version of its documentation can be downloaded from:

http://www.waterplan.water.ca.gov/landwateruse/wateruse/Ag/CUP/California_Clima

te_Data_010804.xls. The model uses weather data, kc of the crop at each growth

stage, soil moisture constants and depletion of soil water from root zone to determine

the amount of water needed to be applied for individual irrigation and the time of its


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application. The model was run using the required input for wheat planted current

weather conditions. A new irrigation schedule was developed. The total amount of

water for each schedule was calculated and compared with the amount of water

measured in the field. CropSyst model was run using the new schedule and the effect

of it on saving irrigation water was assessed.

2.7. Water productivity

Water productivity was calculated for wheat cultivars under the four irrigation

treatments. Furthermore, it was calculated under climate change scenarios and under

using the new irrigation schedule. Water productivity (WP, kg/mm) values were

calculated by the following equation (Pala et al., 1996).

WP = Grain yield (kg)/Irrigation amount (mm)

Results and Discussion

1. CropSyst calibration

1.1. Wheat grain yield

CropSyst model showed good agreement between measured and predicted

values of wheat grain yield (Table 7). Low percentage of difference between observed

and predicted values was obtained, as well as low root mean square error (RMSE).

Furthermore, a close to 1 value of Willmott index of agreement was obtained. Several

publications highlighted the accuracy of the model, such as Benli et al., (2007) and

Singh et al., (2008). Both papers indicated that the model prediction showed low

RMSE. Furthermore, Benli et al., (2007) stated that high Willmott index of agreement

was obtained with a value of 0.98, which is similar to what is shown in Table (7).

Table (7): Measured versus predicted wheat grain yield (ton/ha) grown in 2008/09 and

2009/10 growing seasons

Irrigation 1st growing season 2

nd growing season

Measured Predicted PD % Measured Predicted PD %

I1 6.85 6.84 0.2 6.11 6.09 0.3

I2 6.70 6.67 0.5 6.07 6.05 0.3

I3 6.13 6.11 0.3 5.68 5.66 0.4

I4 5.02 5.00 0.1 4.29 4.27 0.5

RMSE 0.30 0.30

WI 0.98 0.97


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I1=Irrigation with 1.20 ETc; I3=Irrigation with 1.00 ETc; I5=Irrigation with 0.80 ETc; I7= Irrigation

with 0.60 ETc; PD%=Percent difference between measured and predicted yield; RMSE= Root mean

square error; WI= Wilmot index of agreement.

1.2. Wheat biological yield

With respect to wheat biological yield, the model over-predicted its values

under some of the irrigation treatments, where the percentage of change between

observed and predicted values were between -0.45% and +1.54% in the first growing

season. In the second growing season, the value was between -2.51% and +0.60%.

Root mean square error was 0.39 and 0.34 ton/ha for the first and second growing

season, respectively. In addition, Willmott index of agreement was close to 1, i.e. 0.97

and 0.98% (Table 8). Benli et al., (2007), stated that RMSE represented 21% of the

observed average, whereas Willmott index of agreement between the observed and

simulated biomass was 0.76, reasonably close to 1.

Table (8): Measured versus predicted wheat biological yield (ton/ha) grown in

2008/09 and 2009/10 growing seasons

Treatments 1st growing season 2

nd growing season


I1 26.43 26.31 -0.45 27.14 26.46 -2.51

I2 24.38 24.73 +1.44 21.21 20.87 -1.60

I3 23.16 22.63 -2.29 20.09 20.21 +0.60

I4 19.00 19.29 +1.54 16.57 16.44 -0.78

RMSE 0.39 0.34

WI 0.97 0.98 I1=Irrigation with 1.20 ETc; I3=Irrigation with 1.00 ETc; I5=Irrigation with 0.80 ETc; I7= Irrigation

with 0.60 ETc; PD%=Percent difference between measured and predicted yield; RMSE= Root mean

square error; WI= Wilmot index of agreement.

2. CropSyst validation

Validation of CropSyst model for the two cultivars showed good agreement

between measured and predicted grain and biological wheat yield. This agreement

was reflected by low percentage of difference between measured and predicted values

of grain and biological yield, low mean square error and high Willmott index of

agreement (Table 9 and 10).

Regarding to Sakha 94, the root mean square error was low, i.e. 0.46 and 0.43

ton/ha for grain and biological yield, respectively. Willmott index of agreement was

0.96 and 0.95 for grain and biological yield, respectively (Table 9). Singh et al.,

(2008) indicated that CropSyst model was more appropriate than CERES-Wheat in


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predicting growth and yield of wheat under different N and irrigation application

situations, where RMSE was 0.36 ton/ha compared with 0.63 ton/ha for CERES-

Wheat.

Table (9): Measured versus predicted wheat grain and biological yield for Sakha 94.

Irrigation

treatment

Grain yield (ton/ha) Biological yield (ton/ha)


I1 4.8 4.77 0.63 16.8 16.71 0.54

I2 5.8 5.78 0.34 19.1 19.02 0.42

I3 7.0 6.95 0.71 26.3 26.15 0.57

I4 6.9 6.87 0.43 21.4 21.33 0.33

RMSE 0.46 0.43

WI 0.96 0.95 I1= irrigation with 0.6 of ETc; I2=irrigation with 0.8 of ETc; I3= irrigation with 1.0 of ETc; I4=

irrigation with 1.2 of ETc; RMSE= Root mean square error; WI= Willmott index of agreement; PD% =

percent difference between measured and predicted values.

With respect to Sakha 93, the root mean square error was low, i.e. 0.31 and

0.28 ton/ha for grain and biological yield, respectively. Willmott index of agreement

was 0.97 for both grain and biological yield, respectively (Table 10). Lobell and

Ortiz-Monasterio (2006) stated that CERES-Wheat model was able to predict wheat

yield for the different irrigation trials quite well with a RMSE of 0.23 ton/ha.

Furthermore, Singh et al., (2008) reported that RMSE between observed and predicted

biomass by CropSyst was 1.27 ton/ha as compared to 1.94 ton/ha between observed

and predicted biomass by CERES-Wheat.

Table (10): Measured versus predicted wheat grain and biological yield for Sakha 93.

Irrigation

treatment

Grain yield (ton/ha) Biological yield (ton/ha)


I1 5.4 5.36 0.74 18.7 18.62 0.43

I2 5.9 5.87 0.51 20.4 20.33 0.34

I3 7.3 7.26 0.55 27.8 27.74 0.22

I4 7.9 7.84 0.76 27.2 26.99 0.77

RMSE 0.31 0.28

WI 0.97 0.97


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I1= irrigation with 0.6 of ETc; I2=irrigation with 0.8 of ETc; I3= irrigation with 1.0 of ETc; I4=

irrigation with 1.2 of ETc; RMSE= Root mean square error; WI= Willmott index of agreement; PD% =

percent difference between measured and predicted values.

3. Vulnerability of wheat cultivars to climate change

3.1. Effect of A1 climate change scenario

The results in Table (11) indicated that the reduction in the yield of Sakha 93

was lower than Sakha 94 in all time slices. The lowest wheat yield reduction is

expected to occur in 2040, whereas the highest wheat yield losses could be occurred

in 2100 for both cultivars. Wheat irrigated with 1.2 of ETc resulted in lower yield

reduction, compared with irrigation with 0.6 of ETc under the four time slices. The

results in Table (11) also showed that reduction in the yield of both cultivars under

irrigation with 1.0 of ETc was almost close to the percentage obtained with irrigation

with 1.2 of ETc. This result implied that if we want to save on the applied water, we

can apply irrigation using 1.0 of ETc.

Table (11): Percentage of reduction in the yield of wheat cultivars under A1 scenario

for irrigation treatments in the four time slices.

Year Irrigation 2040 2060 2080 2100

Sakha 94 I1 41 52 60 64

I2 34 44 52 56

I3 22 29 35 38

I4 20 28 32 35

Sakha 93 I1 23 31 36 39

I2 20 26 31 34

I3 19 25 29 31

I4 18 25 28 30 I1= irrigation with 0.6 of ETc; I2=irrigation with 0.8 of ETc; I3= irrigation with 1.0 of ETc; I4=

irrigation with 1.2 of ETc.

3.2. Effect of A2 climate change scenario

Similar trend was observed under climate change scenario A2, where

reduction in the yield of Sakha 93 was less than Sakha 94. Furthermore, the highest

yield reduction was obtained in 2040, compared with the other three time slices.

Applying irrigation using 1.2 of ETc resulted in lower yield reduction, compared with

irrigation 0.6 of ETc (Table 12). The results also indicated that in 2040, the reduction

in yield was similar under irrigation with 1.2 and 1.0 of ETc for both cultivars, which


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implied that irrigation with 1.0 could save irrigation water without any more yield

reduction. Ouda et al., (2010) incorporated A2 scenario in CropSyst model and

reported that wheat yield could be reduce by 31% in 2038.

Table (12): Percentage of reduction in the yield of wheat cultivars under A2 scenario



Sakha 94

I1 38 55 71 79

I2 31 47 64 73

I3 20 31 40 52

I4 20 29 37 45

Sakha 93

I1 21 32 46 56

I2 19 28 38 47

I3 18 26 33 43



3.3. Effect of B1 climate change scenario

The yield of Sakha 94 is expected to be reduced by 55% in 2100 when

irrigation was applied with 0.6 of ETc under B1 climate change scenario. Whereas,

the reduction in the yield of Sakha 93 was 32% under the same irrigation treatment.

The reduction in the yield of Sakha 93 was similar under irrigation with 1.2 and 1.0 of

ETc.

Table (13): Percentage of reduction in the yield of wheat cultivars under B1 scenario



Sakha 94

I1 32 42 48 55

I2 26 35 40 47

I3 17 23 28 30

I4 15 21 26 28

Sakha 93

I1 17 23 28 32

I2 15 21 24 28

I3 14 19 24 26



3.4. Effect of B2 climate change scenario


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Similar trend could be obtained under B2 in all time slices for both wheat

cultivars (Table 14). Therefore, it could be concluded that it is possible to save in the

applied irrigation water by applying the irrigation water using 1.0 of ETc. Ouda et al.,

(2010) incorporated B2 scenario in CropSyst model and reported that wheat yield

could be reduce by 27% in 2038.

Table (14): Percentage of reduction in the yield of wheat cultivars under B2 scenario



Sakha 94

I1 33 43 52 58

I2 27 35 44 51

I3 18 24 29 34

I4 17 22 28 32

Sakha 93

I1 18 24 31 36

I2 16 21 26 31

I3 15 20 25 29



The result in Tables (11, 12, 13 and 14) showed that under climate change,

wheat yield was reduced. This result could be attributed to the aboitic stress, such as

heat and water stresses that wheat plants exposed to. High temperature reduces

numbers of tillers (Friend 1965) and spikelet initiation, as well as development rates

(McMaster, 1997). Furthermore, high temperature during anthesis causes pollen

sterility (Saini and Aspinall, 1982) and reduces number of kernels per head, if it

prevailed during early spike development (Kolderup, 1979). The duration of grain

filling is also reduced under heat stress (Sofield et al., 1977), as well as growth rates

with a net effect of lower final kernel weight (Bagga and Rawson 1977 and

McMaster, 1997).

Furthermore, exposing wheat plants to high moisture stress depressed seasonal

consumptive use and grain yield (El-Kalla et al., 1994 and Khater et al., 1997).

During vegetative growth, phyllochron decreases in wheat under water stress

(McMaster, 1997) and leaves become smaller, which could reduce leaf area index

(Gardner et al., 1985) and reduce the number of reproductive tillers, in addition to

limit their contribution to grain yield (Mosaad et al., 1995). Furthermore, water stress


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occurs during grain growth could have a sever effect on final yield compared with

stress occurred during other stages (Hanson and Nelson, 1980).

4. Irrigation water management under climate change

BIS model was run for wheat under a new irrigation schedule was developed.

CropSyst model was run using the new irrigation schedule under the four climate

change scenarios. The results revealed that wheat yield was not changed when the

model was run using the new irrigation schedule under climate change. However,

irrigation water saving was attained.

The results in Table (15) showed that the applied irrigation amount with the

BIS model was higher than the applied irrigation amount using o.6 of ETc. The

simulation of wheat yield under the new schedule indicated that irrigation water

saving could be attained for irrigation with 0.8, 1.0 and 1.2 of ETc. The saved

irrigation amounts were 6, 27 and 39% under irrigation with 0.8, 1.0 and 1.2 of ETc,

respectively (Table 15). These results implied that the growing plants did not make

use of all the applied irrigation water and a certain percentages of that applied water

will be lost in the soil for deep peculation. Under climate change conditions, the

growth of the growing plants is expected to be limited due to the abiotic stress

prevailed, which will reduce the above ground biomass and consequently root water

uptake. Thus, a large amount of the applied irrigation water will be lost.

Table (15): Percentage of difference between measured and predicted irrigation

amounts applied to wheat cultivars (averaged) for the two cultivars.

Irrigation

I1 I2 I3 I4

Amount % Amount % Amount % Amount %

A1 +179 +22 -60 -6 -364 -27 -635 -39

A2 +179 +22 -60 -6 -364 -27 -635 -39

B1 +179 +22 -60 -6 -364 -27 -635 -39

B2 +179 +22 -60 -6 -364 -27 -635 -39 I1= irrigation with 0.6 of ETc; I2=irrigation with 0.8 of ETc; I3= irrigation with 1.0 of ETc; I4=

irrigation with 1.2 of ETc; A1, A2, B1 and B2= four climate change scenarios.

5. Water productivity

The simulation of the application of the new irrigation schedule revealed that

the applied irrigation amount using BIS model not only saved water but also increased


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water productivity under the four climate change scenarios, compared with irrigation

with 0.8 of ETc for the two wheat cultivars. Sakha 93 gave remarkable higher water

productivity, compared with Sakha 94 (Table 16). This result implies that Sakha 93

posse trait of yield stability under abiotic stress. The results also showed that the

highest water productivity will be occurred in 2040 under B1 climate change scenario

and it will decline with increasing time slices.

Table (16): Water productivity (kg/mm) of wheat under irrigation with 0.8 of ETc

versus the new irrigation schedule.

Scenario

Year

Sakha 94 Sakha 93

WPCC WPNS WPCC WPNS

A1 2040 2.72 2.87 6.61 6.99

2060 2.31 2.44 6.07 6.41

2080 1.97 2.09 5.68 6.01

2100 1.80 1.91 5.45 5.76

A2 2040 2.83 2.99 6.71 7.09

2060 2.16 2.29 5.93 6.27

2080 1.47 1.55 5.08 5.37

2100 1.09 1.15 4.35 4.60

B1 2040 3.05 3.22 7.00 7.40

2060 2.68 2.84 6.56 6.94

2080 2.47 2.61 6.26 6.62

2100 2.17 2.30 5.94 6.28

B2 2040 3.00 3.17 6.91 7.31

2060 2.66 2.82 6.53 6.91

2080 2.31 2.44 6.07 6.41

2100 1.99 2.11 5.71 6.04

A1, A2, B1, and B2= four climate change scenarios; WPCC= water productivity under climate change;

WPNS= water productivity under the new irrigation schedule.

Regarding to irrigation amount with 1.0 of ETc, water productivity will

increase when the new irrigation schedule will be used under the four climate change

scenarios for both cultivars. However, water productivity will be the highest under B1

climate change scenario and for Sakha 93 in 2040 (Table 17).




16

Scenario

Year

Sakha 94 Sakha 93

WPCC WPNS WPCC WPNS

A1 2040 3.99 5.46 5.73 7.83

2060 3.61 4.93 5.28 7.22

2080 3.34 4.56 4.99 6.82

2100 3.18 4.35 4.82 6.59

A2 2040 4.07 5.57 5.80 7.93

2060 3.50 4.79 5.18 7.08

2080 3.05 4.18 4.68 6.41

2100 2.38 3.25 3.99 5.46

B1 2040 4.26 5.83 6.04 8.25

2060 3.96 5.41 5.68 7.77

2080 3.69 5.04 5.38 7.36

2100 3.53 4.82 5.21 7.12

B2 2040 4.20 5.74 5.96 8.14

2060 3.87 5.30 5.61 7.67

2080 3.61 4.93 5.28 7.22

2100 3.35 4.59 5.01 6.85



Similar trend was observed under irrigation with 1.2 of ETc, where Sakha 93

gave the highest water productivity under B1 climate change scenarios in 2040 (Table

18).



Scenario

Year

Sakha 94 Sakha 93

WPCC WPNS WPCC WPNS

A1 2040 3.70 6.07 5.11 8.39

2060 3.33 5.46 4.69 7.69

2080 3.14 5.15 4.47 7.34

2100 3.02 4.96 4.34 7.12

A2 2040 3.71 6.09 5.13 8.42

2060 3.28 5.38 4.63 7.60

2080 2.92 4.79 4.23 6.94

2100 2.51 4.11 3.76 6.17

B1 2040 3.92 6.43 5.37 8.81

2060 3.66 6.01 5.07 8.32

2080 3.44 5.64 4.81 7.90

2100 3.31 5.42 4.66 7.65

B2 2040 3.83 6.29 5.27 8.65


17

2060 3.61 5.92 5.01 8.23

2080 3.33 5.46 4.69 7.69

2100 3.16 5.18 4.49 7.37



Conclusion

Rapid changes of climate may seriously inhibit the ability of some crops to

survive or to achieve the desired yield in their current region. Sustainable land and

water management combined with innovative agricultural technologies could mitigate

climate change and help poor farmers adapt to its impacts. New knowledge,

technology and policy for agriculture have never been more critical, and adaptation

and mitigation strategies must urgently be applied to national and regional

development programs. Our results showed that the lowest yield reduction was

obtained for Sakha 93 using irrigation with either 1.0 or 1.2 of ETc under B1 climate

change scenario in 2040. Our results also implied that Sakha 93 was tolerant to the

aboitic stress of climate change compared with Sakha 94 under the four climate

change scenarios. The highest reduction in wheat yield was obtained under A1

climate change scenario in 2100 for the two cultivars (Tables 11-14). The results in

these tables also revealed that irrigation with 0.6 of ETc gave the highest yield

reduction for both cultivars and under the four climate change scenarios.

Under climate change conditions, achieving greater water productivity is the

primary challenge for scientists in agriculture. Therefore, changing irrigation schedule

could provide a cheap and easy to implement irrigation management techniques to

relief the harm effect of climate change, with no additional economic costs. Our

results showed that the application of the new irrigation schedule could save a

remarkable amount of irrigation water (Table 15) and increases water productivity

(Tables 16-18).

In conclusion, to manage water more efficiently for wheat under climate

change conditions, the following procedures should be taken into account:

1. Development of data base to classify the available wheat cultivars according

to their ability to tolerate abiotic stress such as, heat and water stresses, in

addition to document how efficient these cultivars in using irrigation water

under climate change conditions. Our results showed that Sakha 93 is tolerant


18

to heat and water stresses and has use water more efficiently, compared with

Sakha 94.

2. Effect of adaptation strategy, such as changing irrigation schedule, on

reducing the climate change risks should be taken into account during the

management of irrigation water. Our results showed that considerable

irrigation amount could be saved, if irrigation was scheduled using BIS model.

3. Finally, our results suggested that if we want to reduce yield losses for wheat

under climate change conditions and save significant amounts of irrigation

water, Sakha 93 should be cultivated and BIS model should be used to

schedule irrigation.

Acknowledgment

This study part of the activities of " Impacts of Climate Change on Water

Management in Egypt project" funded by Science and Technology Development

Fund, Egypt .

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إدارة مياه الري عن طريق القمح تغير المناخ على الضرر الناتج من تأثير التغلب على

3, تهانى نور الدين 2محمد عبد ربه , 1سميحة عوده البيئهو معهد بحوث األراضى والمياه -قسم بحوث المقننات المائية و الرى الحقلى 1 المعمل المركز للمناخ الزراعى 2 معهد بحوث المحاصيل الحقلية -ا المحاصيل الحقلية قسم بحوث فسيولوجي3

http://www.waterplan.water.ca.gov/landwateruse/wateruse/Ag/CUP/%20California_Climate_Data_010804.xls

http://www.waterplan.water.ca.gov/landwateruse/wateruse/Ag/CUP/%20California_Climate_Data_010804.xls

http://www.bsyse.wsu.edu/faculty/stockle.html

http://coopext.cahe.wsu.edu/~soilsim/

http://www.cru.uea.ac.uk/


21

الملخص العربى

تمت دراسة تأثير تغير المناخ على القمح المنزرع تحت الري بالرش باستخدام بيانات سابقه لموسمين الزراعه

ستخدمت هذه البيانات لمعايرة نموذج 2002/2010و 2002/2002 ثم تم إجراء تجربه حقليه فى. CropSystوا

كانت المعامالت التجربية عبارة عن صنفين من . إلستخدام بياناتها فى التحقق من صحة التنبؤ 2010/2011موسم

واستخدمت أربعة (. من البخر نتح 1.2و 1.0و 0.2و 0.0)وأربع معامالت الري ( 23وسخا 29سخا )القمح

فترات رفى المناخ على محصول القمح فى أربعتغياللتقييم آثار B2)و B1و A2و A1)سيناريوهات للتغير فى المناخ

وكمحاولة لتحسين إدارة المياه تحت التغير فى المناخ تم محاكاة تأثير (. 2100و 2020و 2000و 2090)زمنية

أظهرت . فى محاولة لتوفير كمية مياه الري وتحسين كفاءة استخدام المياه BISتغييرجدول الرى عن طريق إستخدام نموذج

كان قادرا على التنبؤ بمحصول القمح بدرجة عالية من الدقة لكال من إختبارى المعايرة CropSystأن نموذج النتائج

وأشارت النتائج أيضا إلى أنه بوجه عام ان محصول صنفى القمح سوف يتأثر بالتغير فى . والتحقق من صحة التنبؤ

وان اقل نقص فى المحصول تم التنبؤ به فى سنة . 29خا مقارنة بس 23المناخ، ولكن األنخفاض كان أقل بالنسبة لسخا

كما أظهرت . 2100فى سنة A2 أما اكبر نقص فى المحصول تم التنبؤ به تحت سيناريو. B2 سيناريوتحت 2090

٪ تحت الري مع 32و 22و 0النتائج ان إستخدام جدول الرى الجديد تحت ظروف تغير المناخ ادى الى توفير بنسبة

كما ان كفاءة استخدام المياه ارتفعت بعد استخدام جدول الرى الجديد . من البخر نتح على التوالي 1.2و 1.0و 0.2

من البخر نتح ، لكال 1.2و 1.0و 0.2تحت سيناريوهات تغير المناخ األربعة، مقارنة بقيمتها عند إستخدام كميات المياه

وعلى ذلك انه إذا كنا نريد الحد من . على كفاءة استخدام المياهأ 23الصنفين من القمح على حد سواء بينما أعطى سخا

نقص محصول القمح في ظل ظروف التغيرات المناخية وتوفير كميات كبيرة من مياه الري فاننا نوصى بزراعته الصنف

.لعمل جدولة للري BISمع استخدام نموذج 23سخا

Combating the Harm Effect of Climate Change on Wheat Using Irrigation Water Management

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