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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.
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