Simulating yield response of wheat to different irrigation intervals with AquaCrop model

Muhammad Suleman, Muhammad Arshad1, Muhammad Usman1, Abdul Shabbir1 and Muhammad

Idrees

1Department of Irrigation and Drainage, University of Agriculture, Faisalabad.

Abstract

Population of Pakistan is increasing day by day while our water resources are limited as a result

competition of water among different users has increased tremendously. Agriculture being the main consumer of

water, it needs more attention for its use. A study was conducted in the experimental field of University of

Agriculture, Faisalabad, Pakistan to compare the impact of different irrigation schedule on wheat yield. Five

different irrigation intervals (14, 21, 28, 30 and 35) were applied. Calibration of AquaCrop was done using 14-day

of irrigation interval while validation was done with treatments. Parameters which were used in simulation of model

were irrigation data, canopy cover, soil data and data on metrological parameters were also taken. Wheat showed

higher grain yield when irrigation interval was 28 to 35 day of irrigation interval. The yield water use efficiency

showed the increasing trend when interval was increase. The model also showed higher grain yield and biomass

when irrigation interval was increase i.e. when stress increase. The results showed that AquaCrop model perform

well at R2 = 0.928 for grain yield and 0.943 for biomass. The modeling efficiency was 0.55, 0.87 and 0.98 for CC%,

biomass and yield, respectively. However, interval should be fixed according to the field condition because excess

reduction of applied water will have damaging effect on the grain yield. The AquaCrop model is a valuable device

for the future strategies for the management in fixing irrigation interval for wheat crop.

Key words: AquaCrop model, Canopy cover (CC %), Irrigation interval, day (d), Biomass, Yield, Water use

efficiency (WUE)

Introduction

The global demand of water has increased, whereas the available amount of water is limited. As the population

increases it is estimated that with growing population, about up to 3.5 billon people in the world will face severe

water shortage by the year 2025 (FAO, 2001).In Pakistan, per capita availability of surface water has been gradually

dwindling from 5600 m3 per capita in 1947 to 1100 m3 per capita (Bhutta, 2009); this is a threshold for defining “a

water short country” (WAPDA, 2009). Continuous population growth with limited land and water resources has put

enormous pressure on the economy of Pakistan, 40% more food would be required by the year 2025 to feed the

increasing population (Alam and Bhutta, 1996). It will be very difficult in the country like Pakistan to achieve these

targets because of high dependence on irrigated agriculture (Bhutta, 2009).

Under these situations of increasing population and decreased surface water availability, the overall load of water for

crops shift on groundwater resources. The other reason is its availability for all the time to farmers for their crops.

This dilemma is exciting the farmers for their dependence on groundwater. According to WAPDA (2009) the share

of groundwater in country’s agriculture is about 48%. However this increased use of groundwater is deteriorating

our land resources because of its poor quality as compared to surface water. Also our farmers are not well aware of

right use of groundwater for the crops. They just switch on their tube well without taking care of the requirement of

water for crops, which itself depend on crop type, growth stage, soil and season. The result is quick depletion of

aquifers, secondary salinization of soils, increased cost of production and decreased income at farms (Aslam and

Prathapar, 2006).

The above discussion conclude, the precise use of water is the only answer to these problems which is possible with

maximum flexibility in irrigation system, the irrigator should have control of the irrigation interval, water

application flow rate and duration. Through proper irrigation scheduling, it should be possible to apply only the

water which the crop needs in addition to unavoidable seepage and runoff losses and leaching requirements.

Irrigation scheduling can be on fixed days interval (Tunio, 2001).Irrigation scheduling methods are based on two

approaches: a) soil measurements and b) crop monitoring (Hoffman et al. 2005). Irrigation scheduling based upon

crop water status should be more advantageous since crops respond to both the soil and aerial environmental (Yazar

et al. 1999). This water shortage stresses re-scheduling of irrigation which should not affect grain yield significantly

but can reduce the water applied to the crop. Accurate and controlled water application is necessary to achieve the

desired yield and water use efficiency (Al-Kaisi et al. 1997). To achieve the higher irrigation efficiencies and

increase in yield better scheduling should be done according to the field condition (Awan et al. 1991).

Simulation models are very helpful in establishing irrigation plans for different cropping patterns under different

climatic conditions. Simulation models have been used for decades to analyze the crop responses to environmental

stresses and to test alternate management practices by increasing crop water use efficiency (WUE). A number of

computer models are available now a day including DSSAT, LINTUL, WOFOST, MACROS and other agricultural

model have been developed (Van Ittersum et al.,2003). The FAO developed model AquaCrop is thought to be best

for our study because this model deals with water as a main input and it gives crop productivity (yield) as outcome.

The model is expecting to be an effective tool in aiding the development of water management strategies to improve

production and save water. Irrigation scheduling is an option that may increase WUE, optimal scheduling require

good understanding of crop response to water stress (Willian et al. 2008). By scheduling irrigation at different times

with different amounts of water, AquaCrop provides the means to develop irrigation schedules to save water while

minimizing reduction in yield, mostly by saving unnecessary runoff, drainage, soil evaporation and by enhancing

harvesting index (HI) (Hsiao et al. 2009). The simulation was done to study the effect of changes in water related

inputs, making the model particularly suitable for developing irrigation strategies and scenario analysis (Raes et al.

2009).

Wheat, being the major cereal crop of Pakistan, faces periods of water stress/drought due to shortage of water and

seasonal canal closure during the months of December and January. In Punjab, wheat is normally irrigated 4 to 5

times (Mahmood and Ahmed, 2005). First irrigation is given at 15-20 days after sowing at crown root initiation

stage. The subsequent irrigations are provided with an interval of 30 – 35 days. Wheat can be irrigated after the

interval of 10, 15, 21 and 30 days of interval (Ichir et al. 2003; Fizabady and Ghodi. 2004).The adequate water

supply in March is critical to wheat crop. The shortage of soil-moisture at seedling, tillering, pre-flowering and

grain-development stages results in permanent reduction in yield. However, late irrigation at soft dough stage

increased yield by 6% (Tunio, 2001). Thus, there is sufficient room to carry out research to find out what minimum

amount of water should be applied to have maximum yield per millimeter of water applied (Mahmood and Ahmad,

2005).

So, proper irrigation scheduling is needed using pre-estimation of water requirement and yield with AquaCrop

model. The study was helpful for the future demand of wheat and require amount of water to fulfill the crop

requirement. The ease of AquaCrop model is the low requirement of input parameters.

MATERIALS AND METHODS

Study Area

The study area was located in the experimental field of University of Agriculture, Faisalabad, Pakistan with latitude

of 31O- 26' N, longitude of 73O- 06' E and altitude of 184.4m (Ullal et al., 2001).

Field Layout

The experimental area was 2112 m2 (64 m x 33 m). There were total 20 plots with five treatments having four

replication each, with equal size of 94.48 m2 (12.4 m x 7.62 m). The buffer was maintaining at 0.3m between the

adjacent plots.

Crop Husbandry

Rouni irrigation was applied on 28 October, 2010 afterward on 26 November, 2010 sowing of wheat was done. The

recommended seed variety of wheat (Sahar-2006) was sown with seed rate 123.5 kg/ha using seed drill. A basal

dose of fertilizers, as recommended by The Department of Agronomy, University of Agriculture, Faisalabad were

applied which include Nitrogen (N) at 110 kg/ha, Phosphorous (P) at 55 kg/ha and Potash (K) at 60 kg/ha. The full

dose of P and K and half dose of urea were applied uniformly on 27th November, 2010, at the time of sowing.

Remaining dose of urea was applied at time of first irrigation. Spray (herbicide) of wheat was applied whenever

needed.

Table 1: Soil characteristics of the experimental field

Depth (cm)

Sand (% wt basis)

Silt

(% wt basis)

Clay

(% wt basis)

FC

(% vol. basis)

PWP (% vol. basis)

Bulk Density (g/cm3)

0-15 42 41 17 25 11 1.43

15-30 44 41 15 24 11 1.46

30-45 47 39 14 26 10 1.47

Date of sowing, plant population was counted after 10 day of interval till the emergence rate was constant, wheat

was germinated after 7 days of sowing, canopy cover was also measured after 10 day interval by capturing picture

and afterward using Image.J. Software as describe by Raes, (2009), senescence and maximum root zone depth was

also recorded during the experimental trail. Further crop was harvested on 27 April, 2011 before the maturity date

was recorded.

Dry biomass, grain yield and harvesting index were also calculated at maturity. Samples were taken from 1m x 1m

area of each plot. Then put in the field for sun drying. After one day of maturity each sample were weighed with the

help of weigh balance for dry biomass measurement. Afterward threshing was done. Then grains obtained from each

sample were weighted for yield calculation. Harvesting Index was calculated by using the following relation.

HI=(GYB )x 100 ……………… (1)

Where;

HI = harvesting index

GY = grain yield

B = biomass

Treatment Description

The study was planned for assessment of four irrigation treatments having different day (d) of irrigation interval i.e.

T1, T3, T4, T5, in accumulation to control irrigation T2, as illustrated below.

T1 = conventional irrigation interval of 30 days

T2 = 14 days of irrigation interval (control)

T3 = 21 days of irrigation interval

T4 = 28 days of irrigation interval

T5 = 35 days of irrigation interval

Irrigation data

AquaCrop model require input irrigation management file for simulation process. For this purpose plots were

irrigated on fixed days of interval. Cut- throat flume (8 x 3) was installed in the water course to measure the

discharge of water.

Amount of water to be applied for each interval

Amount of water to be applied was measured by taking samples at effective rooting depths i.e. 0-15 cm, 15-30 cm

and 30-45 cm (Panda et al. 2003) individually and then added to get the required amount to be applied, using the

following equation (2).

Thenet amount of water applied=(FC−MCB)/100 x D -------- (2)

Where;

FC = field capacity (on vol. basis)

MCB = moisture content before irrigation (on vol. basis)

D= root zone depth (mm)

Water Use Efficiency

Water use efficiency (WUE) is the amount of crop produced from unit available water (Khurram, 2008). It is

measured in kg.ha-1.mm-1. Mahmood and Ahmed, (2005) studied that Rauni water was not included in the WUE

calculations. To calculate the WUE formula used is:

WUE=CY℘ ………………. (3)

Where;

WUE = water use efficiency (kg.ha-1.mm-1)

CY = Crop yield (kg/ha)

WP = water applied (mm)

Soil Data

Soil texture analysis was done before the experiment as describe by Bouyoucos et al. (1951).Soil data like field

capacity, permanent wilting point and initial soil water content were measured before the experimental trail started

as required for irrigation application and for the model simulation.

Soil Moisture Measurement

Soil moisture content was measured by gravitational method by using the following formula.

M .C=(W w−W d )

Wd∗100 ---------------- (4)

Where;

M.C = Soil Moisture content (in % wt. basis)

Ww = Wet weight of soil sample (g)

Wd = Weight of oven dried soil (g)

Climatic data

Simulation of AquaCrop requires minimum and maximum air temperature (oC), rainfall (mm) and reference

Evapotranspiration (ETo) calculated through ETo calculator. Climatic data was collected from University

Meteorological Station, installed nearer to the field.

Model calibration

The model was calibrated with measured data of the treatment T2 i.e. 14 day of irrigation interval. The parameters

obtain in model calibration were used for validation. The calibrated model was tested with the data measured for T1,

T3, T4 and T5 days of intervals at experimental sites.

Model validation

Data from the T1, T3, T4 and T5 days of intervals at experimental site were used for validating the model. The

validation data set consisted mainly of final aboveground biomass and grain yield. Accordingly, a comparison was

made between the observed and simulated values of corresponding treatments for final aboveground biomass and

grain yield. The observed time progression canopy cover and aboveground biomass was made for treatments.

Performance evaluation of AquaCrop model

The coefficient of determination (R2), root mean square of error (RMSE), % of difference (% D), correlation

coefficient (R), and model efficiency (ME) were used in evaluating the goodness of fit of the AquaCrop model.

a) Model efficiency

Model Efficiency (ME) calculation was based on Eq. (5) (Loague and Green, 1991). ME is a measure of

the robustness of the model.

ME=∑i=1

n

(Oi−O )2−∑i=1

n

( Pi−Oi )2

∑i=1

n

(Oi−O )2 …….……….. (5)

Where;

ME = Model Efficiency

Oi = Observed value

O = Mean observed value

Pi = Simulated/ predicted value

ME ranges from negative infinitive to positive 1; the closer to 1, the more robust the model.

b) Root Mean Square Error

RMSE=√ 1N ∑

i=1

N

(Oi−Si)2 …...……….. (6)

Where;

RMSE = Root mean square error

Oi = Observed value

Si = Simulated value

RMSE (Araya et al. 2010) (Eq.6) indicates to what extent the model over or underestimated the observation

whereas the R2 shows the amount of variance explained by the model as compared to the observed data. R 2 ranges

from 0 to 1. The value closer to 0 is the best estimate.

c) Percentage of Difference

The goodness of fit statistic was %D, the percentage of difference between the predicted (Pi) and observed

(Oi) indicator variables (Ahuja et al., 2000).

%D=Pi−OiOi

x100 ..................... (7)

Where;

% D = % of difference

Pi = Predicted / simulated value

Oi = Observed value

d) Correlation coefficient

The correlation coefficient is an indicator of degree of closeness between observed values and model

estimated values. The observed and simulated values are found to be better correlated as the correlation coefficient

approaches to 1. If observed and predicted values are completely independent i.e., they are uncorrelated then CC

will be zero (Nayak et al., 2005). The correlation coefficient was estimated by the Equation 8.

CC=∑i=1

N

(Oi−Oi ) ( Si – Si )

√∑i=1

N

(Oi−Oi )2∑i=1

N

(Si – Si )2 …….. (8)

Where;

CC = Correlation coefficient

Oi = Observed value

Si = Simulated value

Statistical analysis of data

Effect of different irrigation interval on yield and relationship between yield and WUE was calculated using Statistix

8.1 software. The experimental design was CRD and Least significant difference (LSD) was applied at 5%

probability level to check the significance between treatments mean (Steel and Torrie, 1980).

Results and Discussions

Dry Biomass

The irrigation schedule was based on 14-d (T2), 30-d (T1),

21-d (T3), 28-d (T4) and 35-d (T5) of irrigation interval. Dry

biomass was highest in T2 while T3 and T4 have same

values. T1 and T5 also have similar values. T5 had shown

less dry biomass as compare to the other treatment interval.

The increasing trend is shown in the Fig.1 (b) of all

treatments. Kang et al. 2002 said that by decreasing irrigation time interval there will be high biomass but the yield

was minimized.

3.6 3.8 4 4.23.8

4

4.2

4.4

4.6

f(x) = 1.80251479289941 x − 2.82326183431952R² = 0.928302736748913

Measured yield

Sim

ulat

ed y

ield

Figure 1: Simulated and measured values of all treatments (a) yield (ton/ha) (b) biomass (ton/ha)

12 12.5 13 13.5 14 14.512

12.5

13

13.5

14f(x) = 0.847614560698421 x + 2.31209545603138R² = 0.943426554561522

Measured biomass

Sim

ulat

ed b

iom

ass

Grain Yield

T2, T3 and T4 showed the similar values while T1 and T5 deviated from the previous trend, there was 2.3% and 6%

increase in yield, respectively. The results showed that by increasing the day of irrigation interval yield increases.

Fig.1 (a) shows the trend of yield of all treatment. The present study showed that yield increased by increasing the

day of irrigation interval as said by Balasubramanian and Chari (1982). Irrigation at proper time (Quanqi et al.

2010) and amount will result in good yield of wheat crop. Excess of water had negative effect on the wheat grain

yield (Sun et al. 2006). Ibrahim et al. (2010) said that by

increasing the number of irrigation at same interval will not

have significant effect on yield as seen in case of 14-d of

irrigation interval. As the frequency of irrigation had effect on

the grain yield (Oad et al. 2001).

Water Use Efficiency

T5 showed highest water use efficiency (WUE) while T2 showed lowest efficiency. T1 and T4 had shown the similar

trend. The overall WUE trend was increasing with increase in irrigation day interval as shown in Fig.2. The

experimental results showed that water use efficiency was less when irrigation interval was minimum; these results

are similar to that of Kang et al. (2002) and Zhang et al. (2004).

Figure 2: Relationship between WUE and irrigation interval

7 14 21 28 3502468

101214161820

f(x) = 0.252919762258544 x + 6.62325408618128R² = 0.723898278191413

Irrigation Interval

WUE

Calibration and Validation of AquaCrop model

The model was calibrated using the 14-d of irrigation interval (T2) while T1, T3, T4 and T5 were used for validation.

As evaporation is effected by mulch application so, there were no mulches in experimental field. Crop had no

fertility stress so; soil fertility level was non- limiting. There were no field bunds in the experimental field therefore

runoff was considered. Crop data input used in the model are given in table 2.

Table 2: Crop Parameters for AquaCrop model

Field Experiment Parameters Values

Calibration/validation

Sowing date November 26,2010

Emergence

DAS

DAS

7

Flowering 87

Senescence 120

Maturity 152

Max. rooting depth (cm) 48.29

Values of simulated biomass were close to the measured value when they were correlated with each other but model

shows the highest value than the measured as shown in Fig.3 (a).There was overestimation of CC%, may be due to

the difference between initial soil water content between the measured and simulated values, which results in

overestimation of biomass. Biomass sampling may be one of the factor in the over estimation (Vila et al.2009).

0 40 80 120 1600

2

4

6

8

10

12

14

16

SimulatedMeasured

DAS

Bio

mas

s

0 40 80 120 1600

10

20

30

40

50

60

70

80

90

SimulatedMeasured

DAS

CC

%

Figure 3: Simulated and measured values (a) biomass (ton/ha) (b) CC%

The simulated canopy cover also correlated with the measure values. Figure 3(b) showed that the model under

estimated the canopy cover which may be due to change in initial soil moisture condition with the field condition

(Vila et al. 2009). Results for simulation of CC% showed that model overestimated CC during the growing season

these results were similar to that of Salemi et al. (2011). Further there was a rapid decline shown by the model

indicating that cropping season was short but actually CC did not shown a rapid trend.

Figure 1 (a& b) showed the linear correlation between the measured and simulated dry biomass and grain yield. The

model estimated the yield and dry biomass slightly more than the measured data but overall model gave the good

results with RMSE (< 3.39 CC%, < 0.167 Biomass, < 0.1 yield), ME (> 0.42 CC%, > 0.66 biomass, > 0.97 yield),

R2 (> 0.92 CC%, > 0.98 biomass) and value of CC close to 1, which showed that the model simulated biomass and

yield well. AquaCrop model over predicted yield when compare to measure yield which may be due to ideal

condition used for calibration (Greets, 2008). On the other hand, yield was increased when water stress increases i.e.

increasing day of irrigation interval Balasubramanian and Chari (1982).

The results showed that the most sensitive parameters in AquaCrop model were CC, irrigation (depth and time),

maximum root zone depth, initial soil water content and time of maturity as described by Salemi et al. (2011). Table

3 showed complete statistical analysis.

Table 3: Statistical Evaluation

Statistical Evaluation

Parameters

T2T3

T4

T1

T5

R2 CC%0.99

20.949 0.984 0.919 0.982

Biomass0.98

00.998 0.998 0.994 0.996

RMSECC% 0.68 1.87 2.15 3.391 1.67

Biomass0.15

70.09 0.07 0.167 0.139

Yield 0.04 0.03 0.07 0.09 0.1

Correlationefficiency

CC%1

10.867 1 0.867 1

Biomass1

11 1 1 1

Yield 1 1 1 1 1

Model efficiency CC% 0.63 0.71 0.47 0.42 0.52

Biomass 0.9 0.9 0.98 0.89 0.66

Yield 0.99 0.99 0.98 0.98 0.97

%D Biomass 0.5 3.5 7 8.9 9.7

Yield 0.2 2.3 3.7 5.6 1.6

Conclusion

AquaCrop model uses less input parameters values for its simulation. The simulated grain yield and dry biomass

when correlated with measured they showed good relation between each other, which showed that the model was fit

for the prediction of yield. Statistical results were also verifying the fitness of the model. Statistical results showed

that irrigation interval of 28 to 35 day is fit for good grain yield as compare to 14–d of irrigation interval but the best

fit interval is 30 day.

Recommendation

AquaCrop model can be used for future prediction and evaluation of biomass, yield and water application and their

relationships.

Acknowledgement

The authors sincerely appreciate the Department of Irrigation and Drainage, University of Agriculture , Faisalabad

for assisting in conducting and guiding experiment.

Reference

Ahuja, L.R., K.W. Rojas, J.D. Hanson, M.J. Shaffer and L. Ma. 2000. Root zone water quality model.

Water Resources Publ., High-lands Ranch, CO.

Alam, M. and M.N. Bhutta. 1996. Availability of water in Pakistan during 21st century. Proceedings of the

International Conference on Evapotranspiration and Irrigation Scheduling, November 3-6, 1996, San Antonio,

Texas, USA.

Al-Kaisi, M.M., A. Berrada and M. Stack. 1997. Evaluation of irrigation scheduling program and spring

wheat yield response in southwestern Colorado. Agricultural Water Management. 34:137-148.

Araya, A., S.D. Keesstra and L. Stroosnijder. 2010. Simulating yield response to water of Teff (Eragrostis

tef) with FAO’s AquaCrop model. Field Crops Research 116:196–204.

Aslam, M. and Prathapar, S. A. 2006. Strategies to mitigate secondary salinization in the Indus Basin of

Pakistan: A selective review. Research Report 97. Colombo, Sri Lanka: International Water Management Institute

(IWMI).

Awan, Q.A., A. Sarwar and M.M. Bajwa. 1991. Irrigation scheduling for optimal water use. Pak. J. Agri.

Sci. 28(3):219-222.

Balasubramanian, V., and A.V. Chari. 1982. Effect of irrigation scheduling on grain yield and nitrogen use

efficiency of irrigated wheat at Kadawa and Bakura, northern Nigeria. Nutrient Cycling in Agroecosystems.

4(3):201-210.

Bhutta.1997. National Seminar on “Water Conservation, Present Situation and Future Strategy”. Project

Management & Policy Implementation Unit (PMPIU) of the Ministry of Water & Power.

Bouyoucos, G.J. 1951. A recalibration of the hydrometer method for making mechanical analysis of soil.

J.Agron. 43:434-438.

FAO. 2001. FAO state agriculture data. http//www.apps.fao.org FAO.2001 Water: A Key Resource for

sustainable Development. Report E/cn.17/2001/pc/17. Food and Agriculture Organization. United Nations, Rome.

Italy. http//www.fao.org.

Fizabady, Z.A. and M. Ghodi. 2004. Evaluation of yield and yield components of facultative and winter

bread genotype (Triticum aestivum l).under different irrigation regimes in khorasam province in Iran. Journal of

Agronomy. 3(3):184-18.

Geerts, S., 2008. Deficit irrigation strategies via crop water productivity modeling: field research of quinoa

in the Bolivian Altiplano. Dissertationes de Agricultura 814. Faculty of Bio-Science Engineering, K.U.Leuven,

Belgium.

Hoffman, G.F., T.A. Howell and K.H Solomon. 2005. Management of farm irrigation system. ASCE.

Journal of Central European Agriculture. 6(4):449-460

Hsiao, C.T., L. Heng, P. Steduto, B. R. Lara, D. Raes and E. Fereres. 2009. AquaCrop—The FAO Crop

Model to Simulate Yield Response to Water: III. Parameterization and Testing for Maize. Agron. J. 101:448–459

Ibrahim, M.E., S.M. Abdel-Aal, M.F.M. Seleiman, H. Khazaei and P. Monneveux. 2010. Effect of

different water regimes on agronomical traits and irrigation efficiency in bread wheat (Triticum aestivum L.) grown

in the Nile Delta. Egyptian. J. Appl. Sci. 42:316-322.

Ichir, L.L., M. Ismail and O.V. Cleemput. 2003. Effect of organic and mineral fertilizers on N-use by wheat

under different irrigation frequencies. C.R. Biologies. 326:391–399.

Kang, S., L. Zhang Y. Liang, X. Hu, H. Cai and B. Gu. 2002. Effect of limited irrigation on yield and water

use efficiency of winter wheat in Loess Plateau of China. Agricultural Water Management. 55:203-216.

Khurram, S. 2008. Use of CROPWAT model to improve water use efficiency. M.Sc. Thesis, Faculty of

Agricultural Engineering and Technology. University of Agriculture, Faisalabad,Pakistan.

Loague, K. and D.L. Corwin. 1996. Uncertainty in regional-scale assessments of non-point source

pollutants. p. 131–1 52. In Applications of GIS to the modeling of non-point source pollutants in the vadose zone.

SSSA Spec. Publ. No. 48. SSSA, Madison, WI.

Loague, K. and R.E. Green. 1991. Statistical and graphical methods for evaluating solute transport models: overview

and application. J. Contam. Hydrol. 7:51-73.

Mahmood, N. and R.N. Ahmad. 2005. Determination of water requirements and response of wheat to

irrigation at different soil moisture depletion levels. International Journal of Agriculture & Biology. 7(5):812–815.

Nayak, P.C., K.P. Sudher,D.M. Rangan and K.S.Ramasastri.2005. Short-term flood forecasting with a neurofuzzy

model. Water Resources Research. 41:2-16.

Oad, F.C., S.M. Qayyum, N.L. Oad, G.N. Sohu, A.W. Gandahi and G.Q.Chandio. 2001. Effect of irrigation

frequencies applied at different growth stages on the newly evolved genotypes. Pakistan Journal of Applied

Sciences. 1(3):394-396.

Panda, R.K., S.K. Behera and P.S. Kashyap.2003.Effective management of irrigation water for wheat under

stressed conditions. Agric. Water Manage.63 (1):37-56.

Quanqi, L., D. Baodi, Q. Yunzhou, L. Mengyu and Z. Jiwang. 2010. Root growth, available soil water, and

water-use efficiency of winter wheat under different irrigation regimes applied at different growth stages in North

China. Agricultural Water Management 97:1676–1682.

Raes, D., P. Steduto, T. Chsiao and E. Fereres. 2009. AquaCrop. The FAO crop model to simulate yield

response to water: II. Main algorithms and software description. Journal of Agronomy. 101(3):438-447.

Salemi, H., M.A.M. Soom, T.S. Lee, S.F. Mousavi, A. Ganji and M.K. Yusoff. Application of AquaCrop

model in deficit irrigation management of Winter wheat in arid region. African Journal of Agricultural Research.

6(10):2204-2215.

Steel, R.C.D and J.H. Torrie. 1980. Principles and procedure of statistics. 2nd edition. McGraw Hill Book

Co. New York.

Sun, H.Y., C.M. Liu, X.Y. Zhang, Y.J. Shen and Y.Q. Zhang. 2006. Effects of irrigation on water balance,

yield and WUE of winter wheat in the North China Plain. Agricultural Water Management. 85: 211–218

Tunio, S. 2001. Wheat irrigation scheduling under water shortage conditions, Industry and Economy

Magazine, Pakistan. www.pakistaneconomist.com/issue2001/issue13/i&e4.htm

Ullah, M. K., Z. Habib and S. Muhammad. 2001. Spatial distribution of reference and potential

evapotranspiration across t he Indus Basin Irrigation Systems. Lahore, Pakistan. International Water Management

Institute, Working Paper no. 24.

Van Ittersum M. K., Leffelaar P. A., Van Keulen H. et al. 2003. On approaches and applications of the

Wageningen crop models // European Journal of Agronomy.18: 201–234

Vila, G.M., E. Fereres, L. Mateos, F. Orgaz, and P. Steduto. 2009. Deficit Irrigation Optimization of Cotton

with AquaCrop. Agron. J. 101:477–487.

WAPDA. 2009. Water resource and hydropower development authority vision-2025. Planning and design

division, WAPDA, Pakistan.

William F. S., S. E. Schofstoll and J. Richard Alldredge. 2008. Available water and wheat grain yield

relations in a Mediterranean climate. Field Crops Research. 109:45-49.

Yazar, A., A.T. Howell, D.A. Dusek and K.S. Copeland. 1999. Evaluation of crop water stress index for

LEPA irrigated corn. Irrig. Sci. 18:171-180.

Zhang, X., D. Pei, Z. Li, J. Li and Y. Wang. 2004. Management of supplement irrigation of winter wheat

for maximum profit. Deficit irrigation practices. FAO Water Reports 45, pp.21–34.