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305
AJCS 7(3):305-313 (2013) ISSN:1835-2707
Adoption of an intelligent irrigation scheduling technique and its effect on water use efficiency
for tomato crops in arid regions
Fawzi Said Mohammad1, Hussein Mohammed Al-Ghobari1, Mohamed Said Abdalla El Marazky *1, 2
1Department of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University,
Riyadh 11451, Kingdom of Saudi Arabia
2Permanent address: Agriculture Engineering Research Institute, Agriculture Research Center, P. O. Box 256,
Cairo, Dokki, Giza, Egypt
*Corresponding author: melmarazky@ksu.edu.sa
Abstract
The intelligent irrigation technique is a valuable tool for scheduling irrigation and quantifying water required by plants. This study
was carried out during two successive seasons spanning 2010 and 2011. The main objectives were to investigate the effectiveness of
the intelligent irrigation system (IIS) on water use efficiency (WUE), irrigation water use efficiency (IWUE) and to assess its
potential for monitoring the water status and irrigation schedule of a tomato crop cultivated under severely arid climate conditions.
The intelligent irrigation system was implemented and tested under a drip irrigation system for the irrigation of tomato crops
(Lycopersicon esculentum Mill, GS-12). The results obtained with this system were consequently compared with the control system
(ICS), which utilized an automatic weather station. The results reveal that plant growth parameters and water conservation were
significantly affected by IIS irrigation. The water use efficiency under IIS was generally higher (7.33 kg m-3) compared to that under
ICS (5.33 kg m-3), resulting in maximal water use efficiency for both growing seasons (average 6.44 kg m-3). The application of IIS
technology therefore provides significant advantages in terms of both crop yield and WUE. In addition, IIS conserves 26% of the
total irrigation water compared to the control treatment, and simultaneously generates higher total yields. These results show that this
technique could be a flexible, practical tool for improving scheduled irrigation. Hence, this technology can therefore be
recommended for efficient automated irrigation systems because it produces higher yield and conserves large amounts of irrigation
water. The intelligent irrigation technique may provide a valuable tool for scheduling irrigation in tomato farming and may be
extendable for use in other similar agricultural crops.
Keywords: Smart irrigation; evapotranspiration; tomato yield; water application efficiency; arid region; plant growth parameters.
Abbreviations: AIW- Amount of Seasonal Applied Irrigation Water; Dg- Depth of Irrigation Water; (Dg)t- Total Depth Of
Irrigation Water; Ea- Water Application Efficiency; ICS- Irrigation Control System; IIS- Intelligent Irrigation System; IWUE-
Irrigation Water Use Efficiency; LR- Leaching Requirement; LSD- Least Significant Difference; Qs- Irrigation Discharge; SMS-
Soil Moisture Sensing; WUE- Water Use Efficiency.
Introduction
Tomatoes (Lycopersicon esculentum Mill) are an important
global vegetable crop (Berova and Zlatev 2000), and require
a high water potential for optimal vegetative and reproductive
development (Waister and Hudson, 1970). Production areas
are typically intensively managed with high inputs of
fertilizer and irrigation. Planting tomatoes in Saudi Arabia
accounted for 13% of the total land planted with vegetables
in 2008 (MOA, 2010). Tomato is one of the most important
vegetables because of its special nutritive value, and is the
world’s largest vegetable crop after potato and sweet potato.
Considerable quantities of irrigation water are required,
depending on the soil and weather conditions. To reduce the
total amount of irrigation water needed by a tomato crop
without affecting the yield and fruit quality, the grower must
develop management strategies. To achieve better control and
management of water in tomato production, irrigation
schedules should be based on crop water requirements
according to FAO guidelines (Doorenbos and Pruitt, 1977;
Allen et al., 1998). Another approach is the development of a
daily water balance to calculate ETc and to schedule
irrigation events according to effective soil water storage
capacity and estimated crop water removal. These methods
for irrigation scheduling can be very efficient, but this is
difficult and expensive to implement at a farm level. In most
of the world, irrigated agriculture has been faced with
increased limitations in the water supply over the last few
decades. Major efforts have been made by researchers and
irrigators to increase and to conserve this vital source by
many means. One of these means is the application of
irrigation scheduling using sensors and electronic control
devices. Irrigation scheduling is a technique designed to
accurately give water to a crop in a timely fashion (El-
Tantawy, et al., 2007). Irrigation scheduling methods are
based on two approaches: soil measurements and crop
monitoring (Hoffman et al., 1990). However, the use of more
efficient technologies often increases, rather than decreases,
water consumption (Whittlesey 2003; English et al. 2002).
Improved irrigation scheduling can reduce irrigation costs
and increase crop quality. Irrigation scheduling based upon
crop water status is more advantageous since crops respond
to both the soil and aerial environments (Yazar et al., 1999).
Drip irrigation has been practiced for many years due to its
effectiveness in reducing soil surface evaporation. It has been
used widely for crops in both greenhouses and the field (Du
306
et al. 2008). Uniform water application in drip irrigation is
affected by field topography as well as the hydraulic design
parameters of the drip system such as energy losses in laterals
and emitter characteristics (Mofoke et al. 2004; Yildirim
2007; Zhu et al. 2009). An intelligent irrigation system (IIS)
is integrated with intelligent controllers and uses
microclimate data to schedule water irrigation. Intelligent
irrigation technologies are regarded as a promising tool to
achieve landscape water savings and reduce non-point source
pollution (Nautiyal et al. 2010). This technique is under
evaluation at the trial farm in Dookie, Egypt, and the initial
results indicate up to 43% (average 38%) water savings over
conventional irrigation control methodologies (Dassanayake
et al. 2009). In the past 10 years, intelligent irrigation
controllers have been developed by a number of
manufacturers and have been promoted by water purveyors in
an attempt to reduce over-irrigation (Michael and Dukes
2008). There are many intelligent irrigation systems that
compute the amount of water applied and ET based on
climate conditions (McCready et al. 2009; Mendez-Barroso
et al. 2008; Lozano and Mateos 2007). These systems differ
in their accuracy and reliability. Intelligent irrigation systems
usually depend on modern electronic sensors, which are
capable of collecting data, analyzing and decision making to
start/stop irrigation. These devices generally transmit the
decisions to electronic controller devices, which control the
sprinkler or drip irrigation system. Several moisture sensors
are commercially available, such as tensiometers and
watermarks. They can generally be used for manual readings
to guide irrigation scheduling, while some of them can also
be interfaced directly with the irrigation controller in a closed
loop control system (Zazueta et al., 1994) to automate
irrigation. Some researchers have used tensiometer sensors in
irrigation scheduling for tomato under drip irrigation systems
(Mendez-Barroso et al. 2008; Smajstrla and Locascio1997).
Water use efficiency (WUE) has been reported to decrease
with increased irrigation times and the amount of irrigation
water per growing season (Qui et al., 2008). Several studies
have found that drip irrigation increases yields and WUE by
large amounts compared with those with sprinkler or surface
irrigation (Kamilov et al., 2003; Nazirbay et al. 2007). The
automation of irrigation systems based on soil moisture
sensing (SMS) has the potential to provide maximum WUE.
Such systems maintain soil moisture within a desired range,
which is optimal or adequate for plant growth and/or quality
(Munoz-Carpena and Dukes 2005). Therefore, based on
prevailing conditions and water shortages, the optimum
irrigation schedules for the tomato crop in a region should be
determined. The objectives of this study were to investigate
the effect of different targets of this intelligent irrigation
system on tomato ET, yield, WUE and irrigation water use
efficiency (IWUE) in arid climatic conditions.
Results
Tomato evapotranspiration (ETc)
The processor-interfaced IIS was used as an electronic
controller to monitor, record ETo based on measured weather
parameters and automatically adjust the amount of irrigation
water applied. The daily and weekly averages of the ETc
rates for tomato crops under IIS and ICS treatments were
calculated using the daily records during the two growing
seasons (Table 3). The values of ETc for ICS treatment were
derived by the product of the reference evapotranspiration
(ETo) and the crop coefficient (Kc) for different stages of
tomato crop development. From this table, it can be noted
that the total ETc values for tomato crops under the IIS and
ICS treatments were 540.42 and 671.57 mm, respectively,
with significant water saving equal to 20% with IIS treatment
compared to ICS. Values of ETc during the first four weeks
of crop growth were lower under IIS treatment, then
increased during plant booming and development, peaking
approximately 55 days (8 weeks) post-transplantation. After
this point, values of ETc began to retreat gradually with leaf
senescence, most significantly during weeks 9 to 15, and a
similar trend took place with ICS management. The
accumulated rainfall for the 2010 and 2011 growing seasons
were 14 mm and 16.6 mm, respectively, which are
considered to be not significant for irrigation.
Irrigation management
In IIS treatment, irrigation was scheduled and initiated
automatically based on ETo prediction. This system is
equipped with special options, such as the addition more or
less water depending on the needs of the plant. The water
quantities and timings were monitored and recorded and
shown on the monitor. The ETo values for ICS were
determined using the modified Penman method, FAO version
(Allen et al., 1998) and used efficiently to schedule irrigation
at different growth stages. Based on local experience, these
stages were approximately 30, 40, 40, and 25 days,
respectively, and were considered in the evaluation of Kc.
These stages are: initial, crop development, mid-season and
late season. Furthermore, as shown in Table 3, ETc
determined for the ICS experiment was higher than that of
the IIS, with a similar trend during the entire growth season.
The averages of weekly irrigation water (Dg) added for both
treatments were calculated and tabulated in Table 4. As
shown in this table, the average of total irrigation water used
during the two growing seasons in the IIS and ICS treatments
were 614.26 and 825.47 mm, respectively, with a 26%
difference. Therefore, the results of this study show that IIS
significantly conserves water compared to ICS. Moreover,
the data analysis revealed that ETc values were close in the
initial developmental stages, but their values gradually
diverged during the rest of the season.
Analysis of agronomical characteristics
The effect of IIS scheduling on tomato growth and
productivity parameters were investigated. The growth
characteristics of tomato plants grown during the two seasons
(2010 and 2011) are shown in Table 5. The results of this
study reveal that the IIS had a clear impact on agronomical
plant characteristics. The average plant heights were 45.3 and
38.8 cm for the IIS and ICS treatments, respectively. The
average branch numbers were 6.31 and 5.05 per plant for the
same treatments, and the average yields for the two seasons
were 39.55 and 37.05 ton h-1 for the IIS and ICS,
respectively. The IIS was superior to the ICS in terms of
plant height, number of branches, fruit length, average fruit
weight, early yield, WUE and IWUE by 16%, 26%, 11%,
6%, 8%, 38% and 43%, respectively. In addition, these
results suggest that the tomato yields varied between studied
treatments by 7-9% in favor of IIS.
Water use efficiency
Table 6 illustrates the effects of the IIS and ICS on tomato
water use efficiency during the growing seasons. Through
analysis of this table, we found that the values of WUE and
IWUE were higher in the IIS treatment. For instance,
regarding the first and second seasons in the IIS and ICS
307
Table 1. Metrological data of the experimental site.
2010 Season
Month
Tmax (c°)
Tmin (c°)
MRH %
Total Rainfall mm
SR 104W-2SR
WS (m/s)
ETommday-1
February
26.28
13.40
26.96
0.00
41.29
5.76
4.62
March
30.03
16.39
19.02
0.01
51.51
5.53
5.97
April
32.86
21.41
28.53
0.27
46.01
6.94
6.20
May
37.64
25.25
25.06
0.18
48.22
5.93
6.90
2011 Season
February
23.44
12.41
36.23
0.00
38.71
1.53
4.29
March
25.39
14.77
31.69
0.54
40.34
1.94
5.28
April
30.83
19.86
24.18
0.04
39.59
1.92
6.02
May
35.40
23.29
20.97
0.09
51.63
1.59
6.96
MRH = Maximum relative humidity, SR = Solar radiation.
Fig 1. Schematic diagram of tomato field using drip irrigation systems for both intelligent irrigation (IIS) and control (ICS) systems.
Table 2. Physical properties of different soil layers in the studied field.
BD
g.cm-3
PWP %
m3 m-3
FC %
m3 m-3
Soil texture class
Particle size distribution (%)
Layer depth
cm
Clay %
Silt %
Sand %
1.48
6.83
13.65
Sandy clay loam
19.5
12.0
68.5
0 – 20
1.46
7.17
14.34
Sandy clay loam
20.3
11.0
68.7
20 – 30
1.40
8.33
16.67
Sandy clay loam
26.3
15.0
58.7
30 –60
1.45
7.44
14.89
Sandy clay loam
22.1
12.7
65.3
Average
BD = bulk density, PWP = permanent welting point, FC = field capacity.
Fig 2. The Smart System components used in the study.
308
treatments, these values were 7.50, 6.56 and 7.15, 6.32 kg m-
3, respectively. The tomato yields, in the case of IIS
treatment, were 39 and 40.08 ton h-1 for both seasons,
respectively, and a similar trend was observed for WUE and
IWUE. Moreover, the amounts of applied irrigation water
were 5947.6 and 6337.6 m-3 h-1 for consecutive seasons,
respectively (Table 6). Consequently, the maximum and
minimum values of WUE were 7.50 kg m-3 and 5.33 kg m-3,
respectively. However, the results indicate that irrigation
water was used more effectively through IIS treatment. The
aforementioned table also shows that the highest and lowest
values of IWUE among seasons were 6.56 and 4.30 kg m-3
obtained with IIS and ICS, respectively. The comparison of
the IIS with the ICS shows that the increases in IWUE were
39% and 47% for the 2010 and 2011 seasons, respectively. In
contrast, the smallest amount of irrigation water used was
594.76 mm in case of IIS, while the largest amount applied
was 854.79 mm in the control treatment.
Statistical analysis of agronomical factors
Statistical analysis was conducted using CoHort Software
(2005) program version 6.311. A t- test was used to compare
the average agronomical factors with the two methods,
following a normal distribution. This test was done to find
significant differences between IIS and ICS water treatment.
The results of this test clearly show a large influence of the
IIS technique on tomato agronomical factors in both years.
For instance, the highest amount of irrigation water applied
was detected with the ICS in both seasons, while with the
ICS less water was applied. The data suggest that the IIS
technique had a highly significant effect on the average fruit
weight. However, there was no such effect on either fruit
diameter (cm) or fruit shape. Meanwhile, the agronomical
data for the IIS treatment revealed a significant difference in
plant height (cm), branch number, fruit length (cm), average
fruit weight (g), total yield (kg m-2), total yield (ton h-1) and
WUE/IWUE (kg m-3) compared to the control. WUE and
IWUE were significantly affected by the IIS (p > 0.05) in
both growing seasons, as shown in Table 5. Their averages
were different, depending on the schedule of the IIS.
However, WUE and IWUE ranged during the two seasons
from 5.53 to 7.33 kg m-3 and from 4.50 to 6.44 kg m-3,
respectively. Furthermore, the results presented in Tables 5
and 6 show that both efficiencies under the IIS were higher
than those under the ICS. Maximum values of WUE (7.50
and 7.15 kg m-3) were obtained with the IIS, whereas
minimum values (5.72 and 5.33 kg m-3) were obtained with
the ICS treatment. This result indicates that water was used
more effectively in the IIS. The results also indicate that the
IWUE for IIS was higher than that for ICS treatment. The
maximum values of IWUE (6.56 and 6.32 kg m-3) were
obtained with the IIS in both years, whereas the minimum
values (4.70 and 4.30 kg m-3) were obtained with the ICS.
IWUE was higher with the IIS compared to ICS by 29% and
32% in the 2010 and 2011 seasons, respectively. Thus, the
WUE and IWUE values decreased with increased amounts of
applied irrigation water (Table 6). Furthermore, the higher
respective values (7.50 and 4.75 kg m-3) in the first season
were achieved with the IIS treatment, while the
corresponding values for the second season were 7.15 and
4.30 kg m-3.
Discussion
In this study, marked variation in the ETc of the tomato crop
was seen between the two treatments over the different
seasons; these values were 540.42and 671.57 mm for IIS and
ICS, respectively. This led to a 20% savings in irrigation
water when using intelligent irrigation technology. These
outcomes indicate the importance of adopting IIS due to its
effectiveness in providing irrigation water, which requires
extraordinary effort to obtain especially in arid regions which
suffer from water scarcity, such as Saudi Arabia. As well, this
system will improve irrigation practices and ultimately
minimize labor efforts. In general, this superiority in saving
water may be due to the fact that the IIS has the feature of
increasing or reducing irrigation water according to the needs
of the plants. Despite this, to initiate the process of irrigation
at 80% of ETc, the analysis pointed out that the ETc value of
the control treatment was higher than that of the IIS through
both seasons. Therefore, a comprehensive understanding of
the relationship between the effect of the IIS technique and
water content distribution in the root zone is imperative. This
may be due to the increased accuracy of the irrigation
scheduling which leads to evenly distributed water with
sufficient quantities in the root zone. Moreover, the
differences could have occurred due to application of the
incompatible Kc values which were selected from Allen et al.
(1998) and used for the prediction of ETc. Insignificant
differences were found in the ETc values between treatments
only in the initial development stage, while marked
differences were observed in the other stages, with higher
values under ICS treatment (Table 3). Simultaneously, the
steepness of ETc for the control treatment could have resulted
from an erroneous prediction of ETo, especially when
selecting some coefficients, particularly the crop coefficient,
Kc, and the length of the crop growth stages. Additionally,
the intelligent irrigation system was designed especially for
scheduling landscape irrigation, although it gave satisfactory
results when is used to irrigate a tomato crop. Moreover, the
soil distribution could also be responsible for the ICS results,
since the field consisted of entirely moved soil. The results of
the second season were found to be consistent with the
findings of the first season within each treatment, but a
significant difference was found among treatments. The
consistency was a result of non-significant differences in
microclimatic parameters at the sites of the experiments and
due to minor variations in available soil moisture depletion
levels. The total applied irrigation water, Dg for IIS and ICS,
was 614.26 and 825.47 mm, respectively. This indicates that
there was a 26% savings in irrigation water in the case of IIS
compared to the control treatment. Also, the results indicate
that more irrigation water was utilized under ICS treatment.
Hence, a change in irrigation frequency and application stage
could significantly affect the available soil water during the
tomato growing season. In any case, these amounts are
greater than the amount of irrigation water usually delivered
by the farmers in the area. This study revealed that both
irrigation scheduling techniques had a clear impact on the
agronomical characteristics of the plants. In the same context,
it was found that the average yields for the two seasons were
39.55 and 37.05 ton/ha-1 for the IIS and ICS treatments,
respectively. This shows that the variation between the yields
in the IIS and ICS treatments was 5-9%. The fact that IIS
resulted in greater yields than the ICS can be attributed to
differences in the amount of water applied with the two
treatments. An increased moisture level in the root zone is
vital for increasing the agronomical factors, especially when
more irrigation water was added (Dg) as in the ICS treatment.
The low amount of irrigation water added in the IIS treatment
affected all the agronomical parameters compared to the
control treatment. The results indicate that each 1 mm of
water depth applied by both treatments yielded 65.57 and
63.24 kg/mm for first and second seasons for IIS, while these
308
values were 46.97 and 42.94 kg/mm for ICS. The combined
season averages for the IIS and OCS systems were 64.41 and
44.95 kg/mm, respectively. Conserving water is very
important in areas experiencing severe drought such as Saudi
Arabia. The lower amounts of water used correspond
inversely with higher water use efficiency. This agrees with
the results noted by Faberio et al. (2001) and Almarshadi and
Ismail (2011). Similar findings were also obtained by Oktem
et al. (2003) and Wan and Kang (2006), who found that a low
irrigation frequency resulted in higher water use efficiency
values when compared to a high irrigation frequency.
Generally, IWUE can be increased by reducing irrigation
water losses (Oktemet, al., 2003). Irrigation water use
efficiency can also be affected by soil type, cultural and
management practices (Wan and Kang 2006). Generally, in
IIS, increased yields are obtained while minimal water is
applied, which eventually results in higher IWUE. This
finding is consistent with a study by Sammis and Wu (1986),
who reported that IWUE increased under soil moisture stress,
and is also consistent with the observations of Camp et al.
(1989), Howell et al. (1997), Oktem et al. (2003) and Wan
and Kang (2006), who reported that low irrigation
frequencies result in higher water use efficiency values than
do high irrigation frequencies. For both seasons, the IIS
resulted in higher WUE and IWUE values compared to the
ICS. In general terms, this study suggests that IIS should be
implemented to supply irrigation water to crops in the
required quantity and at the required time. The decreased
WUE and IWUE observed under the ICS treatment can be
attributed to the increasing level of applied irrigation water.
Hence, it can be concluded that the effects on IWUE
accuracy were significant for the IIS, amounting to a 26%
decrease in the amount of seasonal irrigation water required
(Table 6). The same trend was observed for WUE and IWUE,
in which higher values were obtained with the IIS in both
seasons (Tables 5 and 6). Therefore, the IIS resulted in higher
WUE and IWUE values than the ICS. In general, the results
in Table 5 show that all agronomical characteristics of IIS
treatments were significantly superior compared to those of
ICS. The fact that the yield of 2011 was lower with the ICS
treatment could be due to the excess of irrigation water which
was applied.
Materials and methods
Experimental site
Field experiments were performed at the King Saud
University Experimental Farm of the College of Food and
Agriculture Sciences, Riyadh, at 24°43’ N latitude, 46°43’ E
longitude and 635 m altitude during the spring seasons of
2010 and 2011. Generally, the climate in this region is
classified as arid, and the climatological data measured at the
experimental site during this study period are provided (Table
1). The weather station was used to measure the climate
parameters that were used to compute evapotranspiration
(ETo). These values were then compared with the values
obtained from the IIS in the tomato crop fields. The IIS was
programmed in situ, taking into account both the crop type
and environmental conditions of the area. This device was
then calibrated and configured to implement the next phase of
the study prior to collecting real data.
Field features and evaluation of irrigation practices
The study site was divided into two equal plots with a 5 m
buffer in the middle (Fig. 1). Each tomato plot size was 7.2 m
× 12 m (86.4 m2), and the plots were irrigated via nine drip
lines that were 16 mm in diameter at distances of 0.8 m and
mounted with 30 drippers. The distance between drippers on
the line was 0.4 m. The soil type in the plot area was sandy
clay loam; some physical properties of the experimental field
soil are presented in Table 2. One of the two fields was
irrigated automatically with the IIS, while the other was
irrigated manually based on ETc values and using
climatological data from the weather station installed at the
site. The drip irrigation system was installed for both plots
and equipped with controllers to regulate the pressure and a
flow meter to quantify the amount of water added during
each irrigation event. The drip system was evaluated in the
field according to the methodology of ASABE Standard,
S346.1 (2007). The intelligent irrigation system required a
complete database for each station (or “zone”) to be
controlled. It was easy to set up this database with little
effort, and the operator was completely responsible for the
accuracy of both input information and output results from
the database. Every system must be carefully observed and
monitored after initial installation for the best results.
Generally, most systems require adjustment, at the station
level, for some time after installation to provide ideal results.
Evaluation tests were conducted by checking the performance
index values under the operating field conditions. All
evaluation index values were within acceptable limits with
good water distribution uniformity (over 90%). The control
experiment was used for comparison purposes.
Components, functions, and installation of the intelligent
system
The intelligent irrigation system chosen for this study was the
Hunter ET-System.
1
The smart controllers integrate many
disciplines to produce a significant improvement in crop
production and resource management (Norum and Adhikari
2009).This system is not considered the best system, but it
was inexpensive and available on the local market. The IIS
was installed according to the manufacturer’s instructions in
the field for the planned experiments. It can be customized by
station (or “zone”) for specific plants, soils and drip types.
This type of system uses digital electronic controllers and
modules, and its platform can be wired to an ET module that
can sense the local climatic conditions via different sensors
that measure wind speed, rainfall, solar radiation, air
temperature and relative humidity (Fig. 2). The ET module
then receives data from the ET sensor and applies it to the
individual fields (zones) of irrigation. The IIS automatically
calculates crop evapotranspiration (ETc) for local
microclimates based on a modified Penman equation (Allen
et al., 1998) and creates a scientific program that it
downloads to the controller. Here, the ET module was
plugged into the irrigation controller Pro C, which was called
the Controller Intelligent Port, and adjusted the irrigation run
times to only replace the amount of water the plants had lost,
at a rate at which could be effectively absorbed by the soil.
Hence, the IIS relayed data acquisition of environmental
parameters as well as system parameters (pressure, flow,
etc.). The state of the system is compared against a specified
desired state, and a decision as to whether or not to initiate an
action is based on this comparison. In the case of a decision
taken by the ET sensor (Fig. 2) to initiate irrigation, a signal
1The use of the trade name does not imply promotion of this product;
it is mentioned for research purposes only.
309
310
Table 3. Daily and weekly averages of tomato ETc for both systems.
Growth
ETc
ETo
Kc
ETc
Period
for IIS
for ICS
(Week)
(mm/day)
(mm/day)
(mm/day)
1
2.34
4.22
0.70
2.95
2
3.15
4.65
0.70
3.25
3
3.94
4.98
0.93
4.54
4
3.95
5.56
1.15
6.39
5
4.36
5.61
1.15
6.46
6
4.58
5.78
1.15
6.64
7
4.87
5.28
1.15
6.08
8
4.56
5.92
1.03
6.30
9
5.26
6.71
1.03
6.84
10
5.10
6.67
0.90
6.00
11
4.93
6.54
0.90
5.89
12
5.00
6.87
0.90
6.18
13
4.85
6.56
0.83
5.53
14
4.60
6.64
0.83
5.53
15
5.81
7.49
0.90
6.74
16
4.83
6.96
0.75
5.22
17
5.07
7.17
0.75
5.38
Avg.
4.54
5.64
Sum.
540.42
671.57
Table 4. Averages of irrigation water (Dg) and accumulative depths (Dg)t added to the tomato crop using the intelligent and control
systems.
Avg. (Dg) for Tomato, IIS
Avg. (Dg) for Tomato, ICS
Growth
Water
Irrigation Depth
Acc. depth
Water
Irrigation Depth
Acc. depth
Period
Added
Dg
(Dg)t
Added
Dg
(Dg)t
(week)
(m3)
(mm)
(mm)
(m3)
(mm)
(mm)
1
0.65
18.83
18.83
0.89
25.81
25.81
2
0.90
25.94
44.77
0.97
28.09
53.90
3
1.07
30.99
75.76
1.32
38.29
92.19
4
1.12
32.53
108.29
1.93
55.91
148.10
5
1.21
35.08
143.37
1.91
55.15
203.25
6
1.26
36.43
179.80
1.91
55.33
258.58
7
1.35
39.18
218.98
1.82
52.54
311.12
8
1.24
35.87
254.85
1.86
53.78
364.90
9
1.41
40.91
295.76
2.07
59.85
424.75
10
1.42
41.03
336.79
1.84
53.16
477.92
11
1.34
38.78
375.57
1.74
50.28
528.20
12
1.34
38.89
414.46
1.85
53.41
581.61
13
1.35
39.02
453.48
1.61
46.64
628.24
14
1.24
35.78
489.26
1.67
48.39
676.63
15
1.60
46.22
535.47
1.92
55.51
732.14
16
1.31
37.99
573.46
1.60
46.25
778.39
17
1.41
40.79
614.26
1.63
47.08
825.47
Sum
21.23
614.26
28.53
825.47
will be transmitted to open the solenoid valve and pump to
supply the required irrigation water. In the ICS, the climatic
data are gathered from a weather station, and the daily
reference evapotranspiration rate (ETo) is calculated and
utilized in making irrigation decisions. Then, the calculated
ETo data are integrated with the Kc of crops to determine
irrigation water to be added. The determined quantity is fed
manually to the control panel, which in turn transmits a signal
to the solenoid valve to provide the required water to the
field. In some other systems, both soil moisture sensors and
climatic measurements are used. However, the IIS was used
here to irrigate the tomato crops under the drip irrigation
system. Daily tomato ETc data measured from the IIS and
ICS experiments to carry out irrigation were monitored and
recorded. For ICS, the daily ETo measurements were
multiplied by adequate coefficients to provide ETc and used
efficiently to schedule the automated microirrigation systems.
Furthermore, the total ETc for the intelligent and control
irrigation experiments were compared together, and the
overall difference was quite significant.
Agronomic practices and observations
Tomato plants (Lycopersicon esculentum Mill, GS-12) were
transplanted into the fields on February 14, 2010 and
February 7, 2011. The seedlings were planted in a single row
in each bed, with a row spacing of 0.8 m and an interplant
space of 0.4 m per row. Other cultivation practices were
performed following a scheduled tomato crop program. Daily
and weekly ETc rates for tomatoes during the growth period
were determined for the IIS and ICS treatments. The
irrigation water depths (Dg) and accumulative depths added
to the tomato crop under the two treatments were monitored
311
Table 5. Responses of tomato growth yield and water use efficiencies (WUE and IWUE) for irrigation system (IIS and ICS) in the
2012 and 2011 winter seasons.
Treatment
Character
2010 Season
t- sign
2011 Season
t- sign
IIS
ICS
IIS
ICS
Plant height (cm)
44.0
39.0
46.7
38.7
Number of branches
6.0
5.0
6.63
5.10
Fruit length (cm)
6.3
5.7
7.1
6.3
Fruit dia. (cm)
4.6
4.8
5.8
5.1
Fruit shape index
1.23
1.31
1.22
1.30
Avg. fruit wt.(gm)
95.0
93.0
93
84
Early yield (ton ha-1)
23.60
24.00
26.52
22.60
Total yield (ton ha-1)
39.00
37.40
40.08
36.71
WUE ( kg m-3)
7.50
5.72
7.15
5.33
IWUE ( kg m-3)
6.56
4.70
6.32
4.30
, t is significant at 0.05 and 0.01, respectively.
Table 6. Effects of the IIS and ICS on tomato water use efficiency during the growing season.
2010 growing season
Irrigation
treatments
ETc
AIW
WUE
(kg m-3)
IWUE
(kg m-3)
(mm)
m-3 h-1
(mm)
m-3 h-1
IIS
520.30
5203
594.76
5947.60
7.50
6.56
ICS
653.70
6537
796.15
7961.50
5.72
4.70
2011 growing season
IIS
560.50
5605
633.76
6337.6
7.15
6.32
ICS
689.20
6891.80
854.79
8547.9
5.33
4.30
by flow meters and were recorded through the growing
season. The last irrigation was on 31 May and 27 May for the
first and second seasons, respectively. Fruit yield and its
components were evaluated in eight plants from the central
plot rows during the harvest period. Other agronomic
parameters, such as total fruit yield, were recorded for each
plot to obtain the gross yield (t ha-1).
Operation time required
To calculate the ETc and the irrigation water requirements of
tomatoes, daily ETo values were first determined using the
meteorological station and were then multiplied by the crop
coefficients and the water application efficiency. Based on
the area of the field (86.4 m2) and the discharge rate from the
drippers (1.220 l/h), the required water quantity per event and
actual operation time required could be determined.
Accordingly, the actual operation time required could be
calculated based on the following relationship.
)/min(LQ)RL(1E
P)(mA(mm)TEK
min)(L/Q (Lit)V
(min)T sa
w
2
oc
s
(1)
31.2)mm(
o
TE
c
K
60
1.220
)0.101(0.90
0.404.68)mm(
o
TE
c
K
)min(T
(2)
Here, T (min) is the actual operation time required, V (liter)
refers to the water volume to be added, Q (1/min) is the
discharge from the irrigation system, Kc represents crop
coefficient, A (m2) refers to the area of the field, ETo (mm) is
the reference evapotranspiration, LR refers to the leaching
requirement which is equal to 0.1 on the least water area
(Stegman et al.,1980), Pw (40%) refers to the wetted area
percentage and Ea (90%) refers to the water application
efficiency.
Eu.KsEa
(3)
Where Ea = irrigation efficiency coefficient (smaller than 1)
and expresses the ratio: crop root zone to be used by the
crop/applied water. Ks is a coefficient (smaller than 1) which
expresses the water storage efficiency soil (0.9 in sandy soils,
1.0 in clay or loam soils). Eu is a coefficient (smaller than 1)
which reflects the uniformity of water application (a properly
designed and well-managed drip system should reach Eu
values of 0.85-0.95). This coefficient should be measured for
each system regularly (Vermeiren et al, 1980). The net
irrigation requirement Dg must replenish the crop
evapotranspiration (ETc), as rainfall and other components of
the water balance are normally unimportant in the irrigated
area. The gross irrigation requirements (Dg)t must increase
the Dg in order to compensate the irrigation efficiency and to
leach salts.
LR-1Ea
)Dg(Dg
t
(4)
The irrigation system was turned on and off manually in the
control experiments in the ICS plots. The net depth of the
irrigation water (Dg) for IIS under the drip irrigation system
was calculated based on the difference in the flow meter
readings before and after irrigation.
Irrigation water efficiencies
Irrigation water used efficiency (IWUE) was calculated as the
ratio between the total fresh yield (FY) and the seasonal
applied irrigation water (Dg)t (Michael, 1978). Water use
efficiency (WUE) was the relationship between the yield and
the ETc (Wanga et al., 2007). Thus, WUE was calculated as
the fresh tomato fruit mass (kg) per unit land area (Y, kg m-2)
and divided by the units of water consumed by the crop per
unit land area (ETc, m3 m-2, usually reported in mm) to
produce that yield. In this case, WUE is presented in kg m-3,
and crop evapotranspiration Etc can be expressed as the water
depth (mm). Another key parameter for evaluating system
efficiency is the irrigation water use efficiency (IWUE, kg m-
3). The WUE and IWUE were calculated using Equations 3
and 4, respectively.
312
ETc
Y
WUE
(5)
t
gD
Y
IWUE
(6)
In these equations, Y is the economical yield (kg m-3), ETc is
evapotranspiration (mm) and (Dg)t is the amount of
seasonally applied irrigation water (mm). The mature fruits
were harvested once or twice a week, and the plant height
(cm), branch number, fruit length (cm), fruit diameter (cm),
fruit shape index (length/diameter), average fruit weight (g),
and total yield (kg.m-2and ton/h-1) were measured for each
plot at each harvest. The data obtained from the two growing
seasons were tabulated and subjected to variance analysis and
least significant difference analysis (LSD) using CoHort
Software (2005). Treatment mean values were compared
using the least significant difference test (LSD) at a 5%
probability level. Water consumption was considered in this
analysis. Statistical analysis was conducted using CoHort
Software (2005) program version 6.311. A t- test was used to
compare the average of the two methods following a normal
distribution. This test was done to find significant differences
between IIS and ICS water treatment.
Conclusions
The highest actual yield was observed for the IIS (40.08
ton.ha-1 for the second season), which shows the relevance of
this system to field crops, although it was only intended for
scheduling water in landscaping as instructed by the
manufacturer’s manual. As a result of this two-year field
study and using the IIS for irrigation water scheduling, it was
found that the IIS offered a significant advantage in
managing the irrigation of tomato crops in both seasons under
severely arid conditions. In comparison with the control
treatment, the IIS significantly managed water and reduced
irrigation water by 26% due to improved moisture
distribution in the root zone. The lowest amount of water
supplied was recorded with the IIS (614.26 mm), while the
highest value was obtained with the ICS (825.47 mm)
treatment during the two seasons. To verify the findings of
this research, the systems must be assessed for both the same
and different crops at different locations and conditions in
order to reach a well-established result. Until now, not much
scientific work has been carried out on investigating the
compatibility of IIS with field crops, but recently studies
have assessed its suitability. Therefore, the IIS irrigation
method is recommended due to its easy application and
greater water savings. Also, the results indicate that the
values of WUE (7.50 kg m-3) and IWUE (6.56 kg m-3) were
higher with the IIS than the ICS. This result indicates that
water was used most effectively with the IIS treatment. A
high influence of the IIS on tomato yields and agronomical
factors was noted in both years. All agronomical
characteristics of the tomato crops with the IIS were
significantly superior compared to those crops grown under
the ICS. Consequently, the results in both years show that the
IIS had significant effects on WUE and IWUE. The IIS
technique conserved irrigation water by 26% compared to the
amount provided by the control system. Conserving water is
very important in areas experiencing severe drought, such as
Saudi Arabia. This study has demonstrated possible
modifications and developments to the proposed system for
improved and more efficient scheduling control. It can be
concluded that an economic amount benefit can be achieved
with saving large amounts of irrigation water when applying
advance scheduling irrigation techniques such as IIS under
arid conditions.
Acknowledgments
This project was supported by King Saud University,
Deanship of Scientific Research, College of Food &
Agriculture Sciences, Research Center.
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