Journal of Water Resource and Protection, 2013, 5, 681-688 Published Online July 2013 (
Real-Time Modelling and Optimisation for Water
and Energy Efficient Surface Irrigation
Kanya L. Khatri1, Ashfaque A. Memon2, Yasin Shaikh3, Agha F. H. Pathan2,
Sadiq A. Shah1, Kanwal K. Pinjani4, Rabi a Soomro1, Rod Smith5, Zaheer Almani2
1Department of Civil Engineering, Mehran University of E & T, Khairpur Campus, Pakistan
2Department of Civil Engineering, Mehran University of E & T, Jamshoro, Pakistan
3Department of Industrial Engineering, Mehran University of E & T, Jamshoro, Pakistan
4Water Resources Division, National Engineering Services, Lahore, Pakistan
5Faculty of Engineering and Surveying Queensland, USQ Toowoomba, Australia
Received April 15, 2013; revised May 16, 2013; accepted June 17, 2013
Copyright © 2013 Kanya L. Khatri et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The viability and sustainability of crop production is currently threatened by increasing water scarcity. Water scarcity
problems can be addressed through improved water productivity and the options usually presumed in this context are
efficient water use and conversion of surface irrigation to pressurised systems. By replacing furrow irrigation with drip
or centre pivot systems, the water efficiency can be improved by up to 30% to 45%. However, the installation and ap-
plication of pumps and pipes, and the associated fuels needed for these alternatives increase energy consumption. A
balance between the improvement in water use and the potential increase in energy consumption is required. When sur-
face water is used, pressurised irrigation systems increase energy consumption substantially, by between 65% to 75%,
and produce greenhouse gas emissions around 1.75 times higher than that of gravity based irrigation systems so their
use should be carefully planned keeping in view adverse impact of carbon emissions on the environment and threat of
increasing energy prices. With gravity-fed surface irrigation methods, the energy consumption is assumed to be negligi-
ble. This study has shown that a novel real-time infiltration model REIP has enabled implementation of real-time opti-
misation and gravity fed surface irrigation with real-time optimisation has potential to brin g significant improvements in
irrigation performance along with substantial water savings of 2.92 ML/ha which is equivalen t to that given by pressur-
ised systems. The real-time optimisation and control thus offers a modern, environment friendly and water efficient
system with close to zero increase in energy consumption and minimal greenhouse gas emissions.
Keywords: Water Scarcity; Real-Time Optimisation; Furrow Irrigation; Carbon Emissions; REIP
1. Introduction
Worldwide as well as in Australia, irrigated agriculture is
the largest water user, and there is pressure on irrigators
to improve water use efficiency as other sectors compete
for water. One way of improving water use efficiency is
to replace gravity-fed irrigation systems such as border
check and furrow, with more efficient pressurised centre
pivot and drip systems [1,2] because these conversions
can offer a significant reduction in water application at
the field scale, up to 220 mm equivalent to 2.2 ML/ha [3].
Current government policy in Australia encourages the
modernisation of irrigation systems, with the aim of gen-
erating water savings of over 2500 GL per year within
the Murray-Darling Basin, where 85% of Australia’s ir-
rigation takes place. There are limited details regarding
the specific methods for modernisation; however, one
option usually assumed for modernisation will be to con-
vert to pressurised irrigation systems in order to generate
significant water savings.
Irrigation is a primary consumer of energy on farms
[4], so any changes to the irrigation method used can be
expected to change on-farm energy consumption. Direct
energy inputs are primarily the fuel sources used to oper-
ate farm machinery and pumps, while indirect energy
inputs refer to energy that is used to produce equipment
and other goods and services that are used on-farm [5].
Between 23% and 48% of direct energy used for crop
production is used for on-farm pumping [6,7]. Where
pressurised groundwater extraction is used, there is al-
ways more energy required for pumping and delivery to
opyright © 2013 SciRes. JWARP
the field. The energy required for pumping depends on
crop water requirement, total dynamic head, flow rate
and system efficiency [8]. Crops with a higher water re-
quirement result in a larger amount of water being pum-
ped and increase energy consumption that will increase
carbon dioxide and or greenhouse gas (GHG) emissions
and thus adding to environmental degradation. If a grav-
ity-fed irrigation method is used in conjunction with a
surface water source, the energy required to transport and
apply water to the field is negligible [9] and there will be
minimal or zero increase in energy consumption and
minima l carbon di o xide (C O 2) emissions.
Amongst gravity-fed irrigation methods, furrow irriga-
tion is the most commonly used method for irrigating
crops and pastures in northern Australia and around the
world, the energy consumption in surface water resour-
ced regions is assumed to be negligible, but this method
is generally perceived to be low efficient method of irri-
gating crop s, bound by inherent char acteristics and tradi-
tional practices to wasting much of the water applied [10 ].
In fact it is not the fault of method but indeed it is the
lack of proper management and a limited capability to
predict the so il infiltration characteristic.
The performance of surface irrigation is a function of
the field design, infiltration characteristic of the so il, and
the irrigation management practice. However, the com-
plexity of the interactions makes it difficult for irrigators
to identify optimal design or management practices. The
infiltration characteristic of the soil is the most crucial
factor affecting the performance of surface irrigation [10]
and both spatial and temporal variation s in the in filtration
characteristic are a major physical constraint to achieving
higher irrigation application efficiencies. While well de-
signed and managed surface irrigation systems may have
application efficiencies of up to 90% [11], many com-
mercial systems have been found to be operating with
significantly lower and highly variable efficiencies. Pre-
vious research in the sugar industry found application
efficiencies for individual irrigations ranging from 14%
to 90% and with seasonal efficiencies commonly be-
tween 31% and 62%. Application efficiencies in the cot-
ton industry have been shown of similar range and mag-
nitude [12].
A real-time control system has the potential to over-
come the infiltration issues and significant improvements
in irrigation performance up to 30 to 40% are possible
with optimization of individual irrigation events. A study
was undertaken [13] to identify the potential improve-
ment in irrigation performance (application efficiency,
storage efficiency and distribution uniformity) achievable
through real time control strategies. The management va-
riables flow rate and application time required to maxi-
mize the application efficiency were calculated for each
individual irrigation throughout the season. When these
management parameters were optimized using SIRMOD
model for each irrigation throughout the season to simu-
late real-time control of individual irrigations, the aver-
age application efficiency increased significantly to 93%
with a storage efficiency of 90%, without any significant
difference in the distribution uniformity.
The term real time control applied to the analysis of
field parameters in surface irrigation means that irriga-
tion information is collected, studied and processed dur-
ing the irrigation. The results obtain ed are used to modify
the management variables for the same irrigation. The
necessary information can be obtained from advance data
or field run-off. A computer model called SIRTOM (sur-
face irrigation real time optimization model) was devel-
oped [14] to estimate the infiltration parameters in real
time from advance data. The model used a one-dimen-
sional optimization techniqu e to ob tain the parameters (k)
and (fo) of the Kostiakov-Lewis equation. The parameter
(a) was determined separately by the two-point method
[15]. Camacho developed the IPE [16] model for man-
agement and control of furrow irrigation in real time. The
objective was to find the infiltration parameters that
simulate water advance best fitted to the field measured
data. The model estimated the parameters only (k) and a
of the Kostiakov-Lewis equation, where as the parameter
(fo) was to be initially calculated by using indirect meth-
ods. The major drawback of these models is that they are
data intensive and difficult to operate. The IPE model
also requires the final infiltration parameter (fo) to be
measured separately which is time consuming and diffi-
cult to measure accurately. The high data requirement is a
major hindrance to the implementation of any form of
real-time control [17]. To over-come this problem a new
model to prediction of infiltration in real-time (REIP)
that uses a model infiltration curve and a scaling tech-
nique was developed by Khatri and Smith [10]. The
method requires minimum field data, inflow and only
one advance point measured around the mid length of the
furrow. The method has potential for use in real time
irrigation optimisation and control.
This study is a part of research project being con-
ducted for development of a practical surface irrigation
real-time control system at University of Southern
Queensland Australia. In the present paper, the improve-
ments in surface irrigation performance through real-time
optimisation and control are assessed and the water sav-
ings quantified. The savings in energy consumption,
from that required by a change to pressurised irrigation
systems, are estimated. It will be evident that Real-time
optimisation and control of surface irrigation when ap-
plied in conjunction with automation offers a modern
environment friendly, labour and water efficient system
with close to zero energy consumption and minimal
GHG emissions.
Copyright © 2013 SciRes. JWARP
2. Data Analysis
2.1. Description of the Real-Time Optimisation
and Control System
The real-time optimisation and control system involves:
measurement or estimation of the inflow to each fur-
row or group of furrows,
measurement of the advance at one point approxi-
mately mid way down the furrow,
estimation of the infiltration characteristic for the
furrow or group of furrows using the scaling tech-
nique of Khatri and Smith [20],
simulation of the irrigation and optimization to de-
termine the optimum time to cut off the inflow to
achieve improved performance and efficient irrigation
The actual measurement, simulation and control would
preferably be automated but could be undertaken manu-
ally with very little capital investment on the part of the
farmer. A necessary precursor to application of the sys-
tem is the determination of the shape of the infiltration
characteristic (model infiltration curve) for the particular
field or soil type. This is best done from a comprehensive
evaluation of one or more furrows from the field, in-
volving measurements of the inflow, advance and where
possible runoff, with the infiltration curve being deter-
mined using a model such as IPARM [18]. The preferred
(constant) furrow inflow rate is also determined at this
stage although it may be altered over time as experience
with operation of the system is accumulated.
Any infiltration equation can be used however for
consistency with available simulation models the present
study employs the Kostiakov-Lewis equation:
 (1)
where I is the cumulative infiltration (m3/m), a, k, and fo
are the fitted parameters, and τ is the infiltration time
The cumulative infiltratio n cu rv e calculated fro m these
parameters is the model infiltration curve. Subsequently
the model infiltration parameters can be used to estimate
(by scaling) the cumulative infiltration curves for the
whole field, and other irrigation events, using only one
advance point for each of the remaining furrows or for
each subsequent irrigation event.
In this method a scaling factor (F) is formulated for
each furrow or event from a re-arrangement of the vol-
ume balance model (as used by McClymont and Smith
Qt Ax
kt xr
where: Qo is the inflow rate for the correspon ding furrow
(m3/min), Ao is the cross-sectional area of the flow at U/S
end of furrow (m2) (determined by any appropriate
method), t (min) is the time for the advance to reach the
distance x (m) for the corresponding furrow. a, k, f
o are
the infiltration parameters of the model furrow,
y is a
surface shape factor taken to be a constant (0.77), z is
the sub-surface shape factor for the model furrow, de-
fined as:
ar a
where r is the exponent fro m power curve advan ce func-
tion ptfor the model curve of the furrow.
This scaling factor (F) is then applied in conjunction
with the Kostiakov-Lewis infiltration model to scale the
infiltration curves for the whole field as follows:
Fk f
 (3)
where: Is is the scaled infiltration (m3/m), a, k, fo are the
infiltration parameters of the model furrow.
The scaling factor F as given by Equation (2) can be
defined as the ratio between the infiltrated volume as
calculated by a volume balance in the trial furrow at t50
and the infiltrated volume as calculated by the parame-
ters for the model furrow. The application of the factor
(Equation (3)) follows from this definition and assumes
each part (k and fo) of the infiltration function be scaled
in the same proportion.
For the real-time optimisation and control the infiltra-
tion estimates are required in sufficient time to allow
selection and application of optimum times to cut-off
while the irrigation event is under way. To achieve this,
the advance times (t50) taken at the mid-point down the
furrow/field (x50) are used in Equation (2).
2.2. Irrigation Performance and Infiltration Data
Two fields one with maize, field C1; and other one with
cotton field C2, were selected for study and analysis with
a total of 46 furrow irrigation events conducted by grow-
ers using their usual practices, 24 furrow irrigation events
for field C1 and 22 furrow irrigation events for field C2.
These fields were selected from the different commercial
farms across the crop growing areas of Central Queen-
sland for which volume of irrigation water use and irri-
gation advance data have been collected. The basis for
selection was the relatively large number of events for
each field.
Data collected for each irrigation event included:
furrow inflow and outflow rates;
irrigation advance (advance times for various points
along the length of furrow including the time for the
advance to reach the end of the furrow);
physical characteristics of the furrow (length, slope,
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cross section shape).
The flow rate and irrigation advance were measured
using the IRRIMATETM suite of tools developed by the
National Centre for Irrigation in Agriculture, as described
by Dalton [19]. The infiltration parameters for each fur-
row/event for the three fields, estimated by the method of
Khatri and Smith, have been used in simulation and op-
timisation to demonstrate the achievable gains in water
productivity through improved irrigation performance.
2.3. Simulation and Optimisation SIRMOD
(Surface Irrigation Simulation, Evaluation
and Design)
To evaluate the real-time optimization control, simula-
tions were performed for the two fields using the infiltra-
tion parameters in the simulation model SIRMOD [20].
These SIRMOD simulations were used to evaluate the
irrigation performance (application efficiency Ea, re-
quirement or storage efficiency Er, and distribution uni-
formity DU) of the current farm irrigations and assess the
possible potential gains in performance improvement and
volume of water savings ach ieved through real time con-
trol; and comparison of this improvement (Water saving)
to that achieved when systems are converted to centre
pivot and or dri p irri gat i o n.
SIRMOD is a software package designed to simulate
the hydraulics of surface irrigation at the furrow scale,
and to optimize the irrigation system parameters to
maximize application efficiency. The input data required
for the simulation component of the model include field
length, slope, infiltration characteristics, target applica-
tion depth, flow rate, Manning n and furrow geometry.
The model output includes a detailed advance-recession
trajectory, distribution of infiltrated water, volume bal-
ance, runoff hydrograph, water distribution uniformity,
and the water application and requirement efficiencies.
The ability of the SIRMOD to evaluate the irrigation
performance of furrows and borders has been well
documented for example by McClymont [21].
The three performance measures used in the evaluation
have their usual meanings.
Application efficiency Ea is defined as the ratio of
volume of water stored in the root zone during irrigation
to volume of water delivered in the field during that irri-
gation and usually expressed as a percentage.
Requirement (or storage) efficiency Er is a measure of
the adequacy of the irrigation. It is defined as the ratio of
water stored in the root zone during irrigation to water
required (the deficit) in the root zone prior to irrigation.
Uniformity describes the spatial distribution of water
over the field. The performance measure used in this pa-
per, distribution uniformity DU, is def ined as th e aver age
of the lowest 25% of infiltrated depths of water divided
by the average infiltrated depth of water over the whole
Modelling and evalu ation strategies:
To perform the simulations, three (3) irrigation strate-
gies were framed to evaluate the real time optimisation
control and to demonstrate the achievable gains in irriga-
tion performance .
The modelling strategies adopted are:
Strategy 1: Prediction of the current farm irrigation
simulated using the infiltration parameters (REIP a, k, fo),
actual inflow (Qo) and actual cut-off time (tco) as re-
corded under usual farm practices.
Strategy 2: Optimisation of the current farm irrigation.
In this case each irrigation event was optimized b y using
the REIP infiltration parameters and varying the inflow
and cut-off time to obtain maximum application effi-
ciency (Ea). This strategy also determines the best over-
all flow rate.
Strategy 3: A simple practical real-time control strat-
egy in which the REIP infiltration parameters were used
with a fixed inflow rate while varying/optimizing only
the cut-off time to achieve the best irrigation and maxi-
mum wat er saving.
3. Energy Consumption in Irrigation
With increasing demand being placed on water resources
the efficient use of water resources is inevitable to achie-
ve increased food production. Increasing water scarcity
and malfunctioning irrigation systems, now threaten the
viability and sustainability of crop production. Water
scarcity problems can be addressed through improved
water productivity [22] and the option usually supposed
to be in this context is application of pressurised irriga-
tion systems.
The use of pressurised irrigation systems requires sub-
stantial capital investments. In addition, installation and
application of pumps and pipes, and the associated fuels
and oils needed to run them emit significant qu antities of
greenhouse gases. The studies [23,24] show that amount
of GHG emissions from pumping irrigation system is
around 1.47 times higher than that of canal irrigation
systems. It has been shown that around 3.8 kgCO2e and
2.68 kgCO2e of GHG emissions are produced respec-
tively per litre of diesel and oil consumption and have
adverse impact on environment. In case of replacing fur-
row irrigation with drip, cen tre pivot or sprinkler systems,
the water efficiency can be improved up to 30% - 45%
but it does increase en ergy consumption along with GHG
Energy consumption in irrigated agriculture results
primarily from pumping requirements. This is well illus-
trated in a recent case study in Australia by Jackson [25].
They considered five irrigation farms and range of crops
in each of two areas. The first was the Coleambally Irri-
gation Area in southern NSW, where farms are supplied
Copyright © 2013 SciRes. JWARP
with surface water by gravity and the second a ground-
water area in South Australia. The water use and energy
consumption by the current (inefficient) surface irrigation
systems were compared with the reduced water use but
greatly increased energy consumption that would occur if
the surface systems were converted to centre pivot and
drip irrigation. They concluded that in surface water re-
gions investments should target improvements to the
existing surface systems. However in the groundwater
region they recommended adoption of pressurised sys-
tems. In neither case did they attempt to qu antify the wa-
ter or energy savings that would occur from optimised
surface irrigation systems.
Under this research study average water consumption
under surface irrigation application, real-time application,
and when converted to centre pivot and drip irrigation
application have been computed for a field corn crop to
assess the corresponding water saving (ML/ha) and en-
ergy consumption (MJ/ha) under each irrigation applica-
tion system. In this case the water source is a surface
gravity supply and the energy used in the surface irriga-
tion cases is entirely for the cultural operations of land
preparation, sowing, fertiliser, herbicides and harvesting;
and are based on calculation methods as used in other
studies [26-28]. The increased energy consumptions for
the centre pivot and drip systems are a direct result of the
pumping required to give the desired operating pressures.
4. Results and Discussion
4.1. Irrigation Performance
The summary of simulated irrigation performance results
obtained for the model strategies are shown in Ta bles 1
and 2 for fields C1 and C2 respectively. The results ob-
tained under each of the model strategies are discussed
Strategy 1 (Current irrigation-farm management):
From the summary of simulation results for field C1
(Table 1) it is evident that the overall mean irrigation
performance (application efficiency and storage effi-
ciency) of the actual irrigations (strategy 1) was substan-
dard, with mean application efficiency Ea of 52.4% and
storage efficiency Er 95.4%. However, application effi-
ciencies were shown to be highly variable from 40 to
93%. Similarly in case of field C2 the application effi-
ciencies showed considerable variation from 26 to 57%,
but this field showed poorer performance (Table 2) with
an overall mean application efficiency of 42.3% and
storage efficiency of 97.6%.
Strategy 2 (Advanced Real-time Management):
In this case each irrigation event was optimized by
varying inflow (Qo) and cut-off time (tco) to suit individ-
ual soil conditions and furrow characteristics. As ex-
pected an excellent performance was obtained for most
Table 1. Summary of furrow irrigation performance under
different strategies for field C1.
practice Application
efficiency % Storage
efficiency % Distribution
uniformity %
Current fa r mer
Management practice52.4 95.4 92.6
Advanced Real-time
optimisation an d c on trol92.5 92.3 93.4
Simple Real-time
optimisation an d c on trol88.2 90.4 89.2
Table 2. Summary of furrow irrigation performance under
different strategies for field C2.
practice Application
efficiency % Storage
efficiency % Distribution
uniformity %
Current fa r mer
management practice 42.3 97.6 92.3
Advanced Real-time
optimisation an d c on trol88.5 90.5 93.5
Simple Real-time
optimisation an d c on trol85.2 88.6 86.8
events. The mean over all irrigation performance (Ea and
Er) obtained for all of the irrigation events for field C1
was above 92% and for field C2 the Ea was above 88.5%
and Er 92.5% as shown in Tables 1 and 2. This strategy
involves the application of more advanced irrigation
management practices. The overall best flow rate of 6 l/s
as observed under this strategy was selected for use in
strategy 3.
Strategy 3 (Simple Real-time optimisation and con-
From Ta b l e s 1 and 2 it is evident that the simple real
time optimisation and con trol strategy (3) using the REIP
infiltration parameters predicts improved performance
(Ea and Er) for both fields. For field C1 the means of the
performance measures are Ea 88.2% and Er 90.4%, with
mean Ea of 85.2% and Er 88.6% for field C2. The out-
comes from the real time control strategy obtained indi-
cate that simple real-time optimisation and control for
achieving high efficiency in irrigated agriculture is feasi-
ble and significant gains in irrigation performance are
possible from field optimisation system.
4.2. Water Savings from Real-Time Optimisation
and Control
The performance simulation results (Tables 1 and 2)
show there is considerable opportunity to improve the
irrigation performance obtained under current farm prac-
tices (strategy 1). When the real time control (strategy 3)
was applied the overall mean irrigation performance was
improved for both fields. A highly significant improve-
ment in irrigation performance was noted in case of field
C2, with application efficiency increasing from 42.3% to
Copyright © 2013 SciRes. JWARP
85.2% as shown in Table 2, along with acceptable uni-
formity and storage efficiency. It is evident from these
results that the simple real-time control system does have
potential to bring significant gains in irrigation perform-
ance, with the additional benefit of reducing the volume
of water applied per irrigation and deep drainage vol-
umes, thus reducing the potential for environmental
Table 3 presents the total volumes of water applied to
the 46 furrows at fields C1 and C2 under current farm
management and real-time control. It can be seen from
the table that the volume of water applied to the 46 fur-
rows at fields C1 and C2 was reduced from 4036 m3 un-
der usual farm management to 2246 m3 under real-time
control. This indicates the sub stantial potential sav ings of
1590 m3 (1.59 ML) of volume of water per irrigation
(over 3.27 ha), which is a significant loss of water to the
grower. For Queensland cotton growers usually applying
6 irrigations annu ally this represents annual water saving
of 2.92 ML/ha that can be used beneficially to grow fur-
ther crop area, clearly indicating the substantial benefits
that are achievable in the irrigation industry by imple-
menting simple real-time field optimisation and control.
4.3. Energy Consumption in Irrigation
The summary of water savings and energy consumption
in irrigation under different irrigation application systems
is shown in Table 4. It is evident that when real-time
optimisation control was implemented over current sur-
face irrigation the water savings to tune of 2.92 ML/ha
were achieved without increase in energy consumption
which reveals significant water savings with no increase
in carbon emissions, hence real-time field optimisation
and control proved to be water efficient and environment
The table further reflects that when current surface ir-
rigation system was converted to centre pivot and drip
irrigation, a further meager water saving of 0.1 and 0.3
ML/ha was achieved, in comparison to real-time control,
along with highly significant rise in energy consumption
to the tune of 175% and 165% respectively for centre
pivot and drip irrigation application.
A balance between the improvement in water use and
Table 3. Volume of water saving achieved under furrow
irrigation for Field C1 and C2.
Water Applied
under farm
management (m3)
Water applied
under real-time
control (m3)
Water savings
due to real-time
control (m 3)
Field C1 2258 1433 825
Field C2 1778 1013 765
Total 4036 2246 1590
Table 4. Water savings and energy consumption under dif-
ferent irri g a t i on systems.
Irrigation systemWater
Increase in
Current farm
surface irrigation7.52 - 9720 -
surface irrigation4.60 2.92 9720 0
pivot irrigation
application 4.51 3.01 17040 7320
Drip irrigation
application 4.31 3.21 16040 6320
the significant increase in energy consumption is re-
quired. When surface water is used, pressurised systems
increase energy consumption substantially high, so their
use should be planned keeping in view adverse impact of
increased carbon emissions on global warming and threat
of increasing energy prices that may cause farmers to pay
more and save less per hectare, in addition to environ-
ment degradation. Energy consumption is increased more
by installing centre pivot systems than drip systems, be-
cause these systems generally require a higher operating
pressure and are less efficient, resulting in more water
being pumped and hence increasing energy consumption.
5. Conclusions
Under this study, the gains achievable in irrigation per-
formance from real-time optimisation and control of fur-
row irrigation that varies only the time to cut-off, have
been assessed. To evaluate the gains from the system, the
SIRMOD model was used to simulate the irrigation per-
formance for two different fields, under different irriga-
tion modelling strategies using actual farm irrigation data.
The increase in energy consumption that required for a
change to pressurised irrigation has been computed. It is
concluded that:
Real-time optimisation and control is an efficient irri-
gation management tool and has the potential to bring
significant improvement in irrigation performance over
that achieved under current farmer management, and the
substantial reductions in the total volume of water ap-
plied per irrigation achievable.
More importantly, as the system is gravity fed based,
improved performance is achieved without increase in
energy consumption along with zero increase in green-
house gas emissions. Thus real-time optimisation and
control offers a modern, environment friendly, water and
energy efficient system.
Conversion of surface irrigation to modern pressurised
Copyright © 2013 SciRes. JWARP
systems, as an alternative for improved performance,
causes substantial increase in energy consumption so
their use should be carefully planned, keeping in view
the threat of increasing energy prices and adverse impact
of carbon emissions on environmental degradation.
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