Advanced Irrigaion

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Water: Advanced Irrigation TechnologiesCB Hedley, Landcare Research, Palmerston North, New ZealandJW Knox, Cranfield University, Bedford, UKSR Raine and R Smith, University of Southern Queensland, Toowoomba, QLD, Australia

r 2014 Elsevier Inc. All rights reserved.

GlossaryChemigation The injection of water-soluble chemicals,such as herbicides and pesticides, through an irrigationsystem for application to the land using the irrigationsystem.Feedback control A control system that monitors theeffect on the system that it controls and modifies outputaccordingly; for example, a system that controls irrigationapplication rate based on real-time soil moisturemonitoring.Fertigation The injection of water-soluble fertilizerproducts through an irrigation system for application to theland using the irrigation system.

Encyclopedia of Agricult8

Microirrigation Microirrigation, also known as drip ortrickle irrigation, is an irrigation method that drips waterslowly onto plant roots, either via the soil surface or directlyonto the root zone, through a system of pipes, valves,tubing, and emitters.Partial root zone drying A potential water-savingirrigation strategy which irrigates only one side of a rootsystem, potentially saving 50% water with no impact onyield. This is because plant water potential equilibrates withthe wettest part of the soil. There is some evidence to showthat this effect is not long term.

Introduction

Limited opportunities to expand the volume of global fresh-waters allocated to irrigation means that advanced irrigationtechnologies, aiming to improve efficiency of existing systemsare needed, timely, and are of paramount importance.

There is little scope for greater use of allocated globalfreshwaters for irrigation, due to unprecedented expansionsince the 1950s, plus other multiple demands on that resourceto meet higher living standards: projected as þ 400%(manufacturing), þ 140% (thermal electricity generation),and þ 130% (domestic use) by 2050 (OECD, 2012).

Providing for a further 2 billion people by 2050 willchallenge our ability to manage and restore natural assets,including freshwaters, on which life depends (OECD, 2012).Irrigation will need to support a projected 50% increase inglobal food supply to feed the additional 2 billion people(Jury and Vaux, 2007).

Irrigated Areas and Volumes Abstracted

Globally, agriculture is the largest user of freshwater, with irri-gation withdrawals representing approximately three-quarters(70%) of the total freshwater use (Fischer et al., 2007). Of this,only-one half is estimated to reach the crop – the remainderis lost during storage, conveyance, or as subsurface drainage

after application (Jury and Vaux, 2007). In many developingcountries, the proportion used in agriculture is upwards of80% of withdrawals (Turral et al., 2010) highlighting the de-pendence on water for food crop production in rural-basedeconomies (Knox et al., 2012).

Recent food shortages and commodity price spikes haveraised questions regarding food security at both global andnational scales (IAASTD, 2009). In this context, securing ad-equate water of sufficient quantity and quality for agriculturewill be essential in meeting future food demands for a growingpopulation with increasingly diverse dietary requirements.Augmentation methods, such as rainwater harvesting, arenecessary to boost freshwater supplies (see e.g., Figure 1), butimproving the efficiency of use is of paramount importance toassist sustainable use of this resource. Agriculture sits at theinterface between the environment and society, so any increasein water use will need to take into account the consequentimpacts on freshwater ecosystems and the multifunctionalnature and diversity of benefits that irrigated agriculture pro-vides, not just to food production (Knox et al., 2010).

The value of using freshwaters for irrigation should includenot only direct benefits to the party who stands to gainfrom the product but also the wider ecological consequencesof these decisions, and the social goals being served bythe decision (Costanza et al., 1997). Owing to this, therewill be greater government and regulatory demands for

ure and Food Systems, Volume 5 doi:10.1016/B978-0-444-52512-3.00087-5

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Figure 2 Reported rate of growth (% per annum) in the globalirrigated area over the past 200 years. Modified from Jury, W.A.,Vaux, H.J., 2007. The emerging global water crisis: Managing scarcityand conflict between water users. Advances in Agronomy 95, 1–76.

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Figure 1 Rainwater harvesting schemes supplement freshwater allocations for irrigation (Left: Opuha Dam, New Zealand, photo: Opuha WaterLtd.; Right: Arvari River, India). Photo: C. Glendenning

Water: Advanced Irrigation Technologies 379

environmental protection of freshwaters, adding further com-petition for agricultural demands.

Irrigated croplands globally have increased from approxi-mately 100 Mha in 1950 to 275 Mha in 2000 (Lal, 2009),being twice as productive as nonirrigated croplands. However,meeting future food demand will be significantly more chal-lenging than in the 1960s when the first Green Revolutionoccurred, and agricultural efficiency was generally low every-where (Jury and Vaux, 2007). Once surface waters becomefully allocated, communities turn to groundwaters with lesstangible accompanying ecosystem services. The unprecedenteddemands on global groundwaters for food production has ledto overdrafting (rate of extraction4rate of recharge), which iscalculated to be as much as 163 km3 per year, with approxi-mately 80% of this occurring in India and China (Postel,1999). The implications of this are that close to 500 millionpeople are being fed by a water supply that could disappear inthe near future.

Seckler et al. (1999) estimated that up to 25% of India’sgrain harvest could be in jeopardy due to declining freshwaterresources. China increasingly faces water shortage and foodsecurity challenges, with the area of land irrigated in 2003having increased 3.5 times since 1949, and 75% of its graincrop being dependent on irrigation.

Improving Irrigation Efficiency

Increases in the productivity (defined as the amount of yieldper unit of land, ton ha�1) of irrigated land through changes inmanagement and improvements in efficiency offer the greatestpotential for global water savings (e.g., Sadler et al., 2005),because irrigated agriculture is the dominant consumptive userof water.

Seckler et al. (1999) estimated the average irrigation effi-ciency (water required for 100% yield divided by irrigationwithdrawals) for 118 countries around the world in 1990 as43%, and showed that increasing irrigation effectiveness to70% would produce a total water saving of 944 km3 per yearand reduce the need for development of further water suppliesfor all sectors in 2025 by approximately 50%. In reality, when

freshwater resources are limited, it is possible that saved waterwill be directed elsewhere to increase overall productivity. Theefficiency gains enable increased food production, but do notaddress the need to allocate freshwaters for irrigation at asustainable rate. Efficiency gains therefore need to be accom-panied by catchment regulation to maintain a sustainable totalallocation of freshwaters for irrigation (Perry, 2007).

Irrigation efficiency will become increasingly important asconstraints deepen on further expansion of irrigation. There isalso growing evidence that the expansion of irrigated lands,which has been steadily rising since the 1950s, has slowed as weenter the twenty-first century (Figure 2; Jury and Vaux, 2007).

To create efficiency gains, innovative irrigation technologieswill therefore be required:

• more uniform application of water (Burt et al., 1997);

• reduction of evaporation or runoff losses (Burt et al., 1997);

• improvement of sprinklers by lowering the spray to reduceair losses and kinetic energy of impact (Jury and Vaux,2007);

MAC_ALT_TEXT Figure 2


380 Water: Advanced Irrigation Technologies

• improvement of irrigation scheduling and water deliverytiming to reduce water losses, as well as addressing cropsensitivity at certain developmental stages (Jury and Vaux,2007);

• use of soil monitoring and PET estimates to ensure thatcorrect amounts of water are applied at the correct time(Jury and Vaux, 2007);

• correct tillage and field preparation to enhance infiltrationand reduce evaporative losses (Wallace and Batchelor,1997); and

• development of canal linings and other repair measures toimprove the efficiency of water supply from source to field,which is estimated to average approximately 70% globally(Bos, 1985).

Figure 3 Surface irrigation of sugarcane in Swaziland. Gravity fedfurrow irrigation schemes are widespread in Africa although some arebeing converted into either pressurized sprinkler or drip irrigation toimprove efficiency and manage limited water resources. Photo: J.Knox.

Summary

There is much evidence in the scientific literature which con-firms that global agriculture will face a major challenge overthe next few decades – supplying more food to meet increasingdemands while simultaneously reducing its environmentalimpact (Beddington, 2010; OECD, 2012). Dwindling watersupplies, an increasing frequency of droughts, and longer termuncertainties associated with a changing climate, all meanirrigated agriculture needs to do more with less. This impliesboth increasing water productivity (ton ha�1) and raising theeconomic benefits attributed to irrigated production (US$ perm3) (Monaghan et al., 2013).

Farmers and agribusinesses are under pressure to reduceproduction inputs and costs. There are, not surprisingly, anumber of emerging risks – climate change, demands forgreater environmental protection, and increasing competitionfor water resources.

Future advances in irrigation management are likely toprovide still more precision, greater automation, and increas-ingly ingenious and efficient irrigation options to farmers. Thechallenge will be to meet farmer demands for smart irrigationmanagement in an increasingly water-constrained world.

This article therefore discusses the latest technologiesdesigned to improve the application of water by irrigationsystems both at the farm-scale and at larger regional schemes.It presents the latest advances, such as precision tools,remote sensing and web enablement. Finally, it presents aninsight into future needs and trends for advanced irrigationtechnologies.

Technological Advances in Irrigation ApplicationMethods

Section Overview

The traditional irrigation application methods (surface andpressurized) are now dated technologies and are at the limit oftheir irrigation performance under current management prac-tices. Future gains in performance can be achieved boththrough improved design and through the use of advancedtechnologies and management, in particular the use of adap-tive control. The goal is for these adaptive control systems toautomatically and continuously readjust the irrigation

application system to a desired performance, and account forany variability (temporal or spatial) in crop water require-ments or water intake across the field.

This section describes research directed at modernizing theapplication methods, and focuses on both the factors thatlimit performance and the simulation tools and control sys-tems that aim to deliver the needed improvements in thisperformance.

Surface Irrigation

IntroductionIn various types of surface irrigation (e.g., Figure 3), the fur-rows, bays (border dykes), or basins serve both as a meansto convey water across the field and as a surface throughwhich infiltration occurs. The soil infiltration characteristicmore than any other factor serves to determine the level ofperformance or efficiency achievable from surface irrigation.The soil infiltration characteristic can vary both across thefield and also from one irrigation event to the next (Walker,1989; McClymont and Smith, 1996; Emilio et al., 1997;Gillies, 2008). Khatri and Smith (2006) and Gillies (2008)identified this variability as a major physical constraint inachieving higher irrigation performance in furrow-irrigatedfields.

In surface irrigation, infiltration variability causes non-uniformity in water absorption rates and furrow stream ad-vance rates (Trout, 1990). Furrow irrigation efficiency is furthercompounded by the furrow-to-furrow inflow variability inboth gated pipe and siphon tube operated systems (Trout andMackey, 1988). For example, in a typical field under furrowirrigation, it is very difficult to identify one furrow that isrepresentative of the entire field. Therefore, any field evalu-ation of infiltration characteristics based on measurementsfrom only a single furrow is unlikely to give an accurateestimation of irrigation performance (Schwankl et al., 2000;Langat et al., 2008; Gillies, 2008).



Well-designed and managed precision surface irrigationsystems thus have the potential to address both spatial andtemporal variations in soil infiltration through the appropriateuse of simulation, optimization, and adaptation, that is,through real-time control.

SimulationSoftware packages for simulating surface irrigation hydraulicshave been developed to accurately simulate the depth of waterapplied over the field. The two most commonly used modelsto date are SIRMOD (Walker, 1989) and WinSRFR (Bautistaet al., 2009) largely because of their ready availability. Othersimilar models that have been reported include BORDEV andFURDEV (Zerihun and Feyen, 1996) and that of Mailhol andGonzalez (1993).

The software is typically based on a numerical solution ofthe full hydrodynamic (St. Venant) equations (see Walker andSkogerboe, 1989), or, in the case of WinSRFR, of a reducedform of these equations (the zero-inertia approximation). Inall cases their accuracy is limited only by the accuracy of theinput parameters, in particular the soil infiltration parametersand the resistance provided by the surface roughness of thefurrow or bay (represented by the Manning’s n parameter(Limerinos, 1970)).

Depths of infiltration can be calculated at a fine spacingalong the length of a furrow or bay. Across the field the scale isdetermined by the width of the irrigation unit (furrow or bay).In either case, the prediction scale is finer than the scale atwhich applications can be controlled or managed. Therefore,typically an average infiltration characteristic for the entirefurrow or bay is used, and this may lead to infiltration beingunder- and overestimated in many parts of the field (Emilioet al., 1997) due to small-scale variations in the infiltrationcharacteristics. The parameters are usually estimated frommeasurements (inflow, advance, flow depth, and runoff) takenduring an irrigation event. Methods of estimation range fromdirect solution as in the two-point method of Elliott andWalker (1982) to more data intensive but robust methodsinvolving error minimization techniques as in the volumebalance-based Infiltration PARameter Model (IPARM) (Gilliesand Smith, 2005; Gillies et al., 2007) or the multilevel methodof Walker (2005).

The analytical irrigation model of Austin and Prendergast(1997) differs from the other simulation models in that itemploys an analytic solution of the kinematic equations and asimple linear infiltration function. However, its use is limitedto bay irrigation of cracking clay soils, and its accuracy is in-evitably limited in some field situations.

An example of simulation in the improvement of surfaceirrigation performance is the use of the IRRIMATETM suite oftools (Raine and Walker, 1998; Smith et al., 2005), which isnow an accepted practice in the Australian cotton industry.IRRIMATE is a process of field measurement, evaluation,simulation, and optimization that uses data from a measuredirrigation to evaluate the performance of that irrigation and toprovide advice on the best management of future irrigationevents (which in any case could be occurring under differentsoil conditions). The IRRIMATE system currently employsIPARM (Gillies and Smith, 2005; Gillies et al., 2007) to

determine the infiltration parameters from measurements ofthe irrigation advance and runoff. These parameters are thenused in the simulation model SIRMOD in which the opti-mization (selection of the best or preferred irrigation flow rateand/or time to cut off) is a manual trial and error process.These are two significant limitations of the system.

The recently developed Surface Irrigation Simulation Cali-bration and Optimization (SISCO) model, which was appliedin an evaluation of bay irrigation in the Goulburn MurrayIrrigation District of Southern Australia by Smith et al. (2009)and Gillies et al. (2010), removes these limitations. As withearlier models, it employs a solution of the full hydrodynamicequations to simulate the irrigation advance and recession andprovides an estimate of the irrigation performance. However, itis also self-calibrating in that it performs the inverse solutionfor the infiltration parameters from any of a wide selection ofmeasured data including the irrigation advance, runoff, re-cession, and depth data; and optimizes the irrigation againstuser-defined objectives that involve some combination of theusual performance measures.

Understanding and accommodating spatial and temporalvariability of infiltration in furrow irrigation is another uniquefeature of the SISCO model. Given some knowledge of thevariation in the infiltration characteristics across a group offurrows or across a number of irrigation events (e.g., Gillieset al., 2008, 2011), the model allows selection of the flow rateand time to cut off that give the best overall irrigation per-formance for the entire group of furrows.

Automation and control of surface irrigationAutomation and adaptive real-time control has been proposedfor the management of temporal variability of infiltrationcharacteristics (e.g., Emilio et al., 1997; Mailhol and Gonzalez,1993; Khatri and Smith, 2006). It can provide an even higherlevel of irrigation performance than the traditional evaluation(as demonstrated by Raine et al., 1997; Smith et al., 2005;Khatri and Smith, 2007) along with substantial labor savings.

Control systems used in surface irrigation can be imple-mented at diverse levels of sophistication and can be manualor automatic. Automation is not essential to the achievementof efficient surface irrigation; however, it does provide con-venience, reduced labor requirements, and greater certaintyover the control of irrigation durations.

The use of irrigation evaluations to modify future irri-gations (e.g., the IRRIMATE process) is essentially an exampleof temporally separate feedback control where data from oneevent are used to control the next or future events. Real-timecontrol as applied to surface irrigation implies that measure-ments taken during an irrigation event are processed and usedfor the modification and optimization of the same irrigationevent. The real-time control system monitors the advanceof water along the furrow or bay, and through a simulationprocess modifies the management variables (flow rate andtime to cut off) accordingly before the end of that particularirrigation event. If the management variables are continuallyand automatically varied it is a form adaptive control.

Adaptive or real-time control of furrow irrigation poten-tially leads to higher irrigation efficiencies and hence sub-stantial water savings by better matching the irrigation to theprevailing soil conditions.

Figure 4 Rubicon farm channel actuator (left) and bay inlet actuator (right). Photos: Rubicon Water.


Automated feedback control systems have been attemptedfor various configurations of surface irrigation (e.g., Clemmens,1992; Hibbs et al., 1992; Humpherys, 1995a–c; Niblack andSanchez, 2008; Uniwater, 2008). In these cases the responsebeing sensed was the water advance down the field, wherethe sensing was by contact (Humpherys and Fisher, 1995)or noncontact (Lam et al., 2007) means. In most cases, thecontrol systems were able to deliver better irrigation perform-ance (typically measured by higher application efficiencies)than the conventionally managed systems. Furrow irrigationhas seen little automatic control compared with othersurface irrigation techniques. Humpherys (1969) observed thatborder and basin irrigation systems are generally better suitedto automation and control than furrow because the inflow intothe bay is more easily controlled. Some previous attempts atfurrow irrigation automation and control include surge flowirrigation systems (Walker, 1989; Mostafazadeh-Fard et al.,2006), and conventional continuous flow (Hibbs et al., 1992;Lam et al., 2007).

A significant challenge in controlling surface irrigation is toobtain the data needed by the control system in sufficient timeto control the irrigation. An example of this is provided byHibbs et al. (1992), who developed an adaptive control systembased on measurements of the outflow at the downstream endof the furrow. However, the system is impractical because inmost surface irrigation systems the control decisions need tobe made long before the occurrence of any outflow.

All these cases can be considered a form of adaptive controlwhere the response being sensed is the water advance downthe field and the output is the depth of water applied and theusual performance measures of efficiency and uniformity (ra-ther than a crop response). Systems such as these account forthe temporal variation in soil moisture deficits and soilhydraulic properties. Varying the management to accom-modate spatial variations in the soil infiltration characteristic is

usually not considered. Despite the published research, few ifany of these systems have been commercialized.

Recently, Khatri and Smith (2006, 2007) established thebasis for the practical real-time control of furrow irrigation,involving the following:

• continuous measurement of that inflow through inferencefrom measurements of pressure in the supply system;

• measurement of the advance down the furrows at a singlepoint about midway through each irrigation;

• real-time estimation of the soil moisture deficit and thecurrent infiltration parameters from that observation of theirrigation advance; and

• real-time simulation and optimization of the irrigation forselection of the time to cut off that will give maximumperformance for that irrigation.

Preliminary trials of this system, Koech (2012) show thatthe irrigation cutoff times predicted were shorter than thoseused by the farmer in irrigating the remainder of the field. Thistranslated to reduced runoff, deep percolation, and higherapplication efficiencies as a direct result of real-time opti-mization. The system proposed has been kept simple, by usinga fixed inflow and varying only cutoff time, to encourage im-plementation of the system. Although the real-time opti-mization can be operated as a manual system the greatestbenefits occur when it is integrated with automation. Thecurrent phase of development of this system is the integrationwith the Rubicon Water FarmConnects system (Figure 4). TheFarmConnects system combines short-range radio telemetry,solar power, mobile telecommunications, and cloud softwareon the internet to automate and remotely control surfaceirrigation.

In all these systems, the focus is on the control of the in-dividual irrigation event. Although this is an important aspectto improve precision of surface irrigation delivery it is not


Figure 5 Uniform center pivot irrigation on a sugarcane crop (left) (Photo: J. Knox), and variable rate center pivot irrigation for dairy pasture(right). Photo: C.B. Hedley.


sufficient. These systems require comprehensive decisionsupport to provide the seasonal water management that willdeliver maximum water use efficiency.

Sprinkler Systems

Sprinkler or ‘overhead’ irrigation systems deliver water to theplant from above, and may be solid set, motorized with aboom with sprinklers attached, or hand moved, for example,rain guns. In comparison to surface irrigation systems, thesesystems commonly require a power source to move, andprovide greater control of the applied water depths and pos-ition (see Figures 5 and 6).

Figure 6 Overhead irrigation on onions using a mobile hosereelfitted with a boom. These methods are used on high value crops onlight soils where small, frequent applications help to avoid soil andcrop damage. Photo: J. Knox.

Sprinkler pattern simulationPrediction of how adjacent sprinklers overlap to give the pat-tern of application is essential to the design of effectivesprinkler irrigation systems. In its simplest form it involves theoverlapping of known patterns such as in the package Space-Pro (Cape, 1998) to select the nozzle size and spacing for agiven application. Here the objective is to maximize the uni-formity of applied depths. It relies on knowledge of thesprinkler patterns for the given nozzle, pressure, and heightabove ground. Wind effects are typically ignored and the an-swer is relatively insensitive to uncertainties in the individualsprinkler pattern used in the analysis (Christiansen, 1941).

Simulation of sprinkler distribution patterns can not onlyprovide input data for use in models such as SpacePro but alsothe basis for decision-support models for sprinkler systems inthe development and application of optimum irrigationmanagement strategies. Central to an accurate simulation ofsprinkler distribution patterns is the prediction of the impactof wind (speed and direction) on the overlapped pattern. Ingeneral, higher wind speeds lengthen the sprinkler distributionpattern downwind, shorten the distribution pattern upwind,and narrow the distribution pattern normal to the wind dir-ection (Figure 7; Shull and Dylla, 1976). Greater overlap ofadjacent sprinkler patterns is thus required to obtainacceptable uniformity.

Simulation of sprinkler irrigation distribution patterns inwindy conditions has evolved significantly over the past twodecades. Two major approaches have been used: a determin-istic approach, which applies traditional ballistic theory tocalculate the flight trajectories of individual water droplets;and empirical methods, which involve extrapolation frommeasured sprinkler distribution patterns for various windspeeds and directions for the same nozzle, pressure, andtrajectory angle.

An example of the empirical approach is the work ofRichards and Weatherhead (1993) and Al-Naeem (1993),both of whom used measured wind-affected patterns to de-termine six empirical factors that are then used to adjust anyno-wind pattern for that sprinkler to deal with the effects ofwind. This same approach was extended in the TRAVGUNmodel of Smith et al. (2008), which used field-measuredtransects of applied depths from passes of a traveling irrigatorfirst to calculate the no-wind sprinkler pattern and second todetermine the six factors (Figure 8). Output from the model is



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Figure 7 (a) Measured spray pattern for a rain gun sprinkler with a wind speed of 3.58 m s� 1 (left) and (b) predicted spray pattern for a raingun sprinkler with a wind speed of 3.58 m s� 1. Reproduced from Smith, R.J., Gillies, M.H., Newell, G., Foley, J.P., 2008. A decision supportmodel for travelling gun irrigation machines. Biosystems Engineering 100 (1), 126–136.

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an estimate of the uniformity of applications for any selectedwetted sector angle, lane spacing, travel direction, and windspeed and direction. The user can change the various operatingparameters such as the lane spacing and sector angle to iden-tify the optimum values for those parameters.

An advance on this notion was reported by Ghinassi(2010), where the performance of a traveling gun sprinkler ismaximized by real-time variation of pressure, travel speed,wetting angle, and speed of rotation.

The SIRIAS model (Carrion et al., 2001; Montero et al.,2001) reflects the latest thinking in simulation usingsprinkler droplet ballistics. To simulate the wind-affectedpattern for a single sprinkler, SIRIAS requires a radial legpattern measured in still air (for the given sprinkler, nozzleheight, and pressure). The model uses an inverse solution todetermine the droplet size distribution that would give thatsprinkler pattern and then uses that distribution in the pre-diction of the wind-affected pattern. It has been validated fora wide range of nozzles and configurations (e.g., Monteroet al., 2001). The patterns predicted by SIRIAS can then beused in packages such as SpacePro to determine the overlappatterns for whole systems.

For the large mobile center pivot and lateral move systems,models that predict sprinkler patterns to estimate the uni-formity of applications along the machine provide an alter-native simpler method to field trials using large numbers ofcatch cans. However, field trials add value as they also assessmachine maintenance issues, such as blocked sprinklers andhoses. Examples of this type of model are those of Smith(1989) and Thompson et al. (2000). Both used a similarstatistical description of the droplet size distribution andcombined the ballistic model with the overlap along the ma-chine and aggregation of the pattern in the travel direction. Analternative approach was used in the mBOSS model of Foley(2011), who applied the overlap and aggregation to wind-affected patterns imported from SIRIAS.

Ballistic models typically assume that the jet from thenozzle breaks up into the assumed drop size distributioninstantaneously or at some defined distance from the nozzle.In either case drag coefficients are modified in a calibrationprocess designed to make the measured and predictedsprinkler patterns match. In an attempt to overcome this de-ficiency, Grose et al. (1998) used a three-dimensional two-phase plume, which consisted of modeling the interaction ofthe jet with the surrounding air, simulating the separation ofthe jet into individual droplets. However, this approach hasnot gained any acceptance.

Unless the break-up of the stream can be predicted fromthe fundamental fluid mechanics as attempted by Grose et al.(1998), any ballistic model requires a droplet size distributionfor the particular nozzle type and size, and pressure to be usedin the simulation. Obtaining these data is still relatively dif-ficult, time consuming, and expensive.

In all the above models, the usual purpose is estimation ofthe uniformity of applications and the selection of appropriatenozzles and nozzle spacing. Although their accuracy is limitedprimarily by the accuracy of the ballistic models (including theuse of time and vertically averaged wind speeds and dir-ections), they are sufficiently accurate for research and designpurposes. However, none are sufficiently accurate to predict

applications at particular points in an irrigated field withconfidence; hence they are not suitable for use in a farmingdecision support system for precision irrigation.

To counter the adverse effect of wind on sprinkler patterns,Ozaki (1999) developed a prototype robotic self-travelingsprinkler system that controls the nozzle sector and trajectoryangles and the water supply instantaneously in response towindy conditions to minimize the distortion of the sprinklerpattern by wind and the amount of wasted water.

Spatially varied applications – Center Pivot and LateralMove MachinesThe development of mobile sprinkler systems has provided alevel of convenience and efficiency as well as the greatest po-tential for uniform applications, although they need to be welldesigned and maintained to achieve this potential. For ex-ample, in a study of 39 machines, Foley (2011) showed thatless than one-third were operating to specification. In additionthese machines are readily adaptable to deliver spatially variedapplications.

The ultimate performance from these types of machineoccurs through the adoption of low-energy precision appli-cation (LEPA) technology (Lyle and Bordovsky, 1981, 1983).The LEPA system involves the use of very low pressure spraysor bubblers located just above the soil surface on the end oflong drop tubes. Efficiency is improved through the reductionof spray drift and canopy interception and evaporation. Spatialuniformity is also higher than for machines fitted with con-ventional sprinklers.

Research into precision irrigation sprinkler systems wasinitiated in the USA in the early 1990s. Initially this workfocused on the modification of center pivot and lateral moveirrigation machines to apply spatially varied applications ofwater and nitrogen (Evans et al., 1996; Duke et al., 1997;Heermann et al., 1997; Sadler et al., 1997, 2000; Camp andSadler, 1998; Camp et al., 1998; King and Wall, 1998), withthe system control often based on stored databases of spatiallyreferenced data. Readers are referred to Camp et al. (2006) andEvans and King (2012) for comprehensive reviews of researchundertaken in this field. More recently, the emphasis hasshifted to the purpose and performance of spatially varied ir-rigations. Examples of this work include King et al. (2005),Sadler et al. (2005), Camp et al. (2006), Chavez et al. (2006),and Dukes and Perry (2006). Perry et al. (2003) showedsubstantial amounts of water conservation for center pivots,and later Han et al. (2009) developed and tested equipmentand software for variable rate irrigation (VRI) application ofwater using a lateral move irrigation system.

An interesting use of a system designed for spatially variedapplications was provided by Chavez et al. (2010). In this case,the spatially variable capacity was used to compensate fornonuniformity inherent in the irrigation applications from themachine by providing greater uniformity.

Various technologies have been used to deliver VRI appli-cations, including

• multiple discrete fixed-rate application devices operated incombination to provide a range of application depths (seeMcCann et al., 1997; Camp and Sadler, 1994);

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Figure 10 (a) Drip irrigation used for pasture establishment in South Australia. (b) Taps control water supply to the drip tape installed at 0.2 mdepth, with pressure compensating emitters at 0.4 m spacing releasing water at 1 l h� 1. Photos: L. Finger.

Node Node Node Node

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Figure 9 Schematic diagram of individual sprinkler control on a center pivot using GPS and wireless node technology (Available at: www.precisionirrigation.co.nz).


• flow interruption to fixed-rate devices to provide a range ofapplication depths that depend on pulse frequency (seeEvans and Harting, 1999); or

• variable-aperture sprinklers with time-proportional control(see King and Kincaid, 2004; King et al., 1997).

Research to date has resulted in the development ofprototype systems for variable rate application, with increasingcommercial uptake of these products in the past few years.Appropriate decision-support systems, particularly those thatincorporate the outputs from real-time monitoring technolo-gies have not reached an equivalent stage of development.Evans et al. (1996) acknowledged that the greatest difficultyfaced in the implementation of spatially varied irrigationis associated with determining appropriate prescriptions forthe application of water and nutrients. This issue is discussedfurther in Section Technological Advances In IrrigationManagement.

Examples of commercial systems for control of variableapplications from center pivot machines are the Farmscan7000 VRI system developed in Australia and a similar systemdeveloped in New Zealand by Precision Irrigation (Precision

Irrigation, 2014). The New Zealand system was released intothe market in 2008, and incorporates individual sprinklercontrol using wireless nodes and GPS technology (Figure 9).

Microirrigation Systems

Microirrigation systems are typically designed to wet only thesoil zone occupied by plant roots and to maintain this at ornear an optimum moisture level, using emitters spaced alongdrip lines. The obvious advantages of microirrigation include asmaller wetted surface area, reduced evaporation from the soilsurface, reduced weed growth, and potentially improved waterapplication uniformity within the crop root zone by bettercontrol over the location and volume of application (see e.g.,Figure 10).

A particular benefit of microirrigation (also known as dripor trickle) is the ability to apply small amounts of water atshort intervals. This provides scope to maintain the soil at aspecified moisture content for part or all of the season andhence the opportunity for increased effectiveness of rainfallduring the irrigation season. However, the low soil moisture



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Figure 11 Application efficiencies varying with the method of scheduling irrigations for drip-irrigated vines in the Sunraysia region of Victoria,Australia. Reproduced from Schache, M., 2011. Identifying best management practice through irrigation benchmarking: Would you like probes withyour drippers? In: Irrigation Australia, 2011 Irrigation & Drainage Conference, Launceston, Tasmania. Mascot, NSW, Sydney: Irrigation AustraliaLimited.


deficits maintained under such systems also limit opportun-ities for any excess rainfall to be stored in the soil. This canreduce the amount of effective rainfall and exacerbate runoffand nutrient leaching.

The potential efficiency of microirrigation systems is oftenquoted as being greater than 90%. Losses of water in micro-irrigation systems occur principally through evaporation fromthe soil surface, surface runoff, and deep drainage. Evaporationlosses are generally small due to the limited wetted surfacearea and the absence of ponded surface water due to the lowdischarge rates. The application of water usually occurs be-neath the crop canopy, either directly onto or beneath the soilsurface, further reducing the potential for evaporative loss.Runoff losses are also usually small due to the low applicationrates. However, as with all irrigation systems, the ability toachieve high levels of efficiency is more a function of themanagement of the system rather than some inherent propertyof the system. For example, Shannon et al. (1996) found thatdrip irrigation application efficiencies under commercial con-ditions in the Bundaberg area ranged from 30% to 90%. Giventhe nature of the system, these losses were most likely fromoverirrigation and deep percolation. Similarly, Schache (2011)found application efficiencies of drip-irrigated vineyards in theSunraysia region of Southern Australia to range from 25% to100% (Figure 11). In this case, the identified causal factor wasthe method used for irrigation scheduling, with grower ex-perience (subjective approaches) faring worst when comparedwith more scientific (objective) methods. Similar experienceswere also observed in drip-irrigated crops under supplementalirrigation conditions (Knox and Weatherhead, 2005).

Placement of the drip lines is an important considerationin achieving high efficiencies. For example, Henderson et al.(2008) demonstrated a 25% gain in efficiency when drip lineswere placed adjacent to each row of broccoli rather than be-tween every second row.

Dominant causes of nonuniform applications undermicroirrigation systems are pressure variations along the lateralpipelines, variability in the emitters occurring during manu-facture, and blockage of the emitters. Extensive evaluations ofthe uniformities of applications from microirrigation systemshave been conducted in the USA (e.g., Hanson et al., 1995)using mobile field laboratories. These have shown that emissionuniformities are less than desirable with commercial systemscommonly operating with an emission uniformity (Eu) of lessthan 80%. This is supported by Australian data from McCly-mont et al. (2009) and Hornbuckle et al. (2009), who reporteddistribution uniformities as low as 32% from a sample of drip-irrigated vineyards in Southern Australia. These observationshighlight the need for improved design and in-field evaluation,diagnosis, and correction of microirrigation systems if theirpotential for precision irrigation is to be realized.

Systems for recording and reporting the results of per-formance evaluations of microirrigation systems are available,for example, Hornbuckle et al. (2009). However, these do notprovide diagnostic capability and cannot be readily integratedwith the software used for system management.

Microirrigation systems also have greater potential for ac-curate irrigation delivery than other systems. They are easilycontrolled and are commonly automated on a time, soilmoisture, or time–temperature basis (e.g., Phene and Howell,



1984; Meron et al., 1996; Dukes and Scholberg, 2004; Wanjuraet al., 2004; Evett et al., 2006). They also lend themselves toadaptive control and have the potential to apply spatiallyvariable applications at a range of scales from individual lat-erals to individual emitters. Variable rate-controllers that re-spond to real-time sensing and decision making areparticularly applicable to microirrigation systems. They havenot been used to apply water variably down an individuallateral or drip line, and would require additional modificationfor this to occur.

Research into precision irrigation for microirrigation sys-tems has been undertaken primarily in horticultural cropsincluding viticulture (Ooi et al., 2008; Capraro et al., 2008a,b)and fruit tree orchards (Coates et al., 2004; Uniwater, 2008;Adhikari et al., 2008).

Capraro et al. (2008a,b) utilized closed-loop irrigationcontrol systems with moisture measurements in the root zonesto maintain the soil moisture level around a set value. Regu-lated deficit irrigation (RDI) strategies were incorporatedwithin the irrigation control system to achieve particularquality targets, that is, the enological quality of the grapes.

Coates et al. (2004, 2005, 2006) focused their efforts on thedevelopment of a spatially variable microsprinkler system thatwould allow for management of individual trees in an orch-ard. More specifically, the objective was to supply water anddissolved chemical fertilizers differentially to one or more in-dividual trees fed by a single microsprinkler drip line.

Preliminary results show that spatially variable manage-ment at this scale is possible. Another example of sensor-basedcontrol of spatially varied applications from a microsprinklersystem is provided by Torre-Neto et al. (2000).

More recently, Ooi et al. (2008) developed and tested anautomated irrigation system for microirrigation. Two irrigationcontrollers – a soil-moisture-based controller and an ET-basedcontroller – were integrated into a wirelessly networked irri-gation control system in an apple orchard and a commercialvineyard. Results have shown that automated irrigation usingclosed-loop control systems improved water productivity by73% compared with manual irrigation (Uniwater, 2008).These results demonstrate the potential of closed-loop irri-gation control for irrigators at the lower end of the spectrum to‘leapfrog’ rapidly to the upper end of the efficiency spectrum.For those irrigators already at the upper end of the spectrum,adoption of the technology would lead to substantial laborand time savings.

Technological Advances in Irrigation Management

Section Overview

This section covers the following aspects:

• advances in irrigation scheduling,

• soil moisture mapping,

• wireless sensor networks (WSNs), and

• precision irrigation management.

The technological advances in irrigation systems describedin Section Technological Advances in Irrigation ApplicationMethods must be accompanied by state-of-the-art decision

support tools to determine when to turn on the irrigator andhow much irrigation to apply; aiming to maximize any benefitsgained from the investment in new technology. Decision-support tools either evolve alongside equipment developmentbecause the new equipment provides new and enabling meth-ods for optimizing irrigation timing, placement, and amounts,or they may develop independently and be appropriate foruse with a range of different types of irrigation systems.

Advanced irrigation scheduling (Section Section Overview)therefore aims for accurate placement of optimized amountsof irrigation at critical times. This, in turn, is primarily deter-mined by crop water demand and soil moisture supply;and new sensor technologies are being used to define cropdemand and soil moisture supply at high spatial and temporalresolution (Greenwood et al., 2010). Knowledge of dailycrop water demand is useful, but monitoring soil moisturesupply to crop provides a predictive tool for scheduling.Section Advances in Irrigation Scheduling discusses these latestdevelopments in soil moisture mapping; Section Soil MoistureMapping describes methods to update static maps at regulartime intervals.

Recent WSN technological advances and their commercialavailability provide the means for site-specific monitoring toinform irrigation management decisions. These technologicaladvances include smart integration of sensors, wireless nodes(for communication), internet- and cellular-enabled transfer,and processing and reporting protocols. A specific example,the sensor web enablement (SWE), an initiative of the OpenGeospatial Consortium (OGC) will be discussed in SectionWireless Sensor Networks.

The term ‘precision irrigation’ reflects the precision agri-culture concept, applying GPS with sensors to prescribe inputsin the right place, at the right time, and in the right amount.Precision agriculture addresses in-field variability, largely ig-nored until the 1980s, and new GPS-enabled technologies areenabling precise irrigation management tools, which will bediscussed in Section Wireless Sensor Networks.

Advances in Irrigation Scheduling

Designers typically plan for the peak flow rate of a new irri-gation system to meet the seasonal crop water requirementsfor the area to be irrigated plus any freshwater allocation re-quirements. Once in place, appropriate scheduling tools arethen used to assess seasonal changes to daily evapotranspira-tion (ET) demands from specific crops, and irrigation is thenscheduled accordingly.

Advanced technologies for assessing regional ET lossesinclude remote sensing by satellite or airborne scanners(e.g., Gonzalez-Dugo et al., 2006). Multispectral satellites,such as advanced very high resolution radiometer, moderateresolution imaging spectroradiometer (MODIS), and Land-sat TM (Thematic Mapper), have been used since the 1970sto estimate ET. These systems are generally limited by theirspatial (30 m–1 km pixel) and spectral (5–36 band) reso-lutions. However, maturation of imaging spectrometrytechnology combined with greater availability of airborneimaging spectrometer data present new opportunities forimproved accuracy of ET estimates by airborne remote


sensing. These researchers tested the ‘gold standard’ of air-borne imaging spectroscopy, NASA’s airborne visible/infra-red imaging spectrometer, which has 224 spectral bands at10-nm intervals, and concluded that imaging spectrometersare suitable for determining ET and understanding associ-ated physiological processes, although they are limited byspatial extent, and at present are most appropriate for re-gional estimates rather than site (field)-based estimates.

Site-specific irrigation scheduling is frequently based onsoil water balance models that determine a daily soil moisturedeficit using soil, crop, climate, and latitude inputs (Allenet al., 1998), with a modeled daily ET value. A critical soilmoisture deficit is used for timing irrigation events. Suchtraditional modeling tools are very useful but do not easilyaddress commonly encountered within-field variability, andcannot easily account for factors such as ponding due to soilcompaction, high water tables, variable crop growth due toshading, etc. Advanced scheduling tools need to addresswithin-site variability due to these factors, that is, topographicrelief, short-range changes in soil depth, texture and moisturestorage capability and management effects including tillage,fertility, pests, and various irrigation system characteristics(Sadler et al., 2005; Green et al., 2006; De Jonge et al., 2007;Evans et al., 2012) and technologies are emerging to addressthe challenge (e.g., Peters and Evett, 2007, 2008).

Site-specific crop stress measurements (Green et al., 2006;Peters and Evett, 2007, 2008) have been trialed for improvedirrigation scheduling. Peters and Evett (2008) used a ‘tem-perature-time-threshold method.’ Crop leaf temperature isused as an indicator of crop stress, which is measured on afully automated center-pivot irrigation system, where infraredthermocouple thermometers are attached to the trusses of thepivot. A field datalogger is accessed once a day to assesswhether canopy temperature is above threshold level. Anothernovel method determines time for irrigation from crop stressassessed indirectly through soil moisture measurements. Onsetof crop stress is indicated by a reduced apparent daily cropwater uptake (Thompson et al., 2007).

Soil moisture monitoring tools for triggering irrigation areperhaps the most widely used and most important tools forirrigation scheduling (Fang et al., 2007) and a range of newimproved sensors for monitoring soil water are now available(Cardenas-Lailhacar et al., 2010). Improved accuracy of soilmoisture sensors is obtained by site-specific calibration andensuring good soil contact on installation (Greenwood et al.,2010). Recent advances have been made to link soil moisturemonitoring sites automatically to software decision toolslinked to irrigation systems. Blonquist et al. (2006) installeda soil moisture sensor (time domain transmission) to logvolumetric soil water content compared with an irrigationthreshold, and connected this to a solenoid valve on the irri-gation line supplying water to the irrigation system. This sys-tem applied 53% less water than under the conventionalmethod. Kim et al. (2009) also linked soil moisture moni-toring equipment to software control of a site-specific pre-cision linear-move sprinkler irrigation system.

Other technologies have been developed for other methodsof irrigation application. In surface irrigation schemes, wherefarmers receive a fixed amount of water during a fixed period,site-specific scheduling is limited. Here regional scheduling

tools become important; and GIS-integrated tools have beendeveloped for equitable irrigation supply to account for vari-ability in soil and crop conditions, unreliable intake of waterinto the main canal, absence of storage reservoirs, and unevendistributions of water into tertiary canals (Rowshon et al.,2009). The GIS tool links field irrigation demand predictionsand then simulates and recommends optimal irrigation supplystrategies in the Tanjung Karang Irrigation Scheme for ricegrowers in Malaysia.

Australian researchers have developed an integrated modelfor simulating border-check irrigation of dairy pastures thatcombines a biophysical model of the soil–plant–climateinteraction with a hydraulic irrigation model, which modelsinfiltration and movement of water through the soil matrix(Douglas et al., 2010). This model was used to assess howpasture production varied with irrigation management, such asirrigation duration, to improve overall scheduling of irrigationwithin the scheme.

An Australian review of software tools for on-farm watermanagement (Inman-Bamber and Attard, 2005) lists a numberof irrigation scheduling software packages that are increasinglybeing integrated into irrigation control via web and cellularcontrol systems. Hornbuckle et al. (2009) describes a remotesensing method for assessing within-field crop health variations(using NDVI) and links this to reference ET values from nearbyweather stations to provide field-specific scheduling infor-mation. This crop coefficient derivation process uses a shortmessage service (SMS) to provide information through a simplemobile phone text message service to irrigators on a daily basis.Such technologies enable real-time adaptive control systems forirrigation application (Smith et al., 2010). Adaptive controlmeans that scheduling parameters are based on feedback fromthe process (Fig. Smith et al., 2010, p. 62) aiming for continuedsystem improvements.

These scheduling methods assess crop and soil status, aswell as other management effects – regional and some sitespecific – to improve scheduling tools. Site-specific measure-ments are obviously preferable and the next section explainshow mapping tools can be combined with site-specific meas-urements to (1) optimize positioning of the sensors and (2)provide a map of soil or crop condition to add further re-finement to decision support tools and technologies for irri-gation scheduling.

Soil Moisture Mapping

Recent technological advances in GPS-enabled proximal(ground-based) sensing methods are providing rapid afford-able mapping methods of land, soil, and crops to inform ir-rigation management decisions. For example, electromagneticinduction (EM) surveys typically use very accurate positioningequipment (e.g., real-time kinematic differential GPS, RTK-DGPS), quantifying soil spatial variability at resolutions ofo10 m, and simultaneously providing a digital elevation map(DEM) with an accuracy of o0.1 m.

The EM sensor measures soil apparent electrical conduct-ivity (EC) which is influenced by soil texture and moisture innonsaline soils (e.g., Sudduth et al., 2005; Brevik et al., 2006).Soil EM maps provide the basis for targeted soil sampling

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Figure 12 (a) Soil EC map, (b) available water-holding capacity map, and (c) derived soil water status map. Reproduced from Hedley, C.B., Yule,I.J., 2011. Soil water status maps for variable rate irrigation. In: Clay, D., Shanahan, J., Pierce, F. (Eds.), GIS Applications in Agriculture – NutrientManagement for Improved Energy Efficiency. Third Book in CRC GIS in Agriculture Series. Boca Raton, FL: CRC Press, pp. 173–190.


strategies to sample the full range of likely soils encountered inthe area of interest. An area of 50 ha can easily be mapped inone day, by pulling the sensor behind an all-terrain vehicle,with on-board GPS, datalogger, and field computer.

Topographic features that are likely to influence irrigationefficiency (e.g., slope, aspect, and slope angle) can be derivedfrom the DEM, and used in conjunction with EC to deriveoptimal sampling and monitoring positions (Minasny andMcBratney, 2006).

The EM map is not only used to select soil moisturemonitoring sites, but can also be used to calibrate soil ECvalues against soil available water-holding properties (Waineet al., 2000; Godwin and Miller, 2003; Hedley and Yule, 2008;Hedley and Yule, 2009) so that a soil available water-holdingcapacity map can be produced, for spatial irrigation scheduling(Figure 12). Hezarjaribi and Sourell (2007) also used EMmapping to define zones for targeted soil sampling to assesssoil AWC.

Triantafilis et al. (2009) describe how EM surveys em-ploying root-zone sensing Geonics EM38 and vadose-zonesensing Geonics EM31 sensors are related to subsurface soilproperties such as texture, moisture, and depth to water table;and are used to define management classes for precisionmanagement. Sherlock and McDonnell (2003) found thatEM38 data could explain470% of gravimetrically determinedsoil moisture variance.

Primary terrain attributes derived from the DEM, collectedas part of the EM survey, or by other means, include surfacederivatives such as slope, aspect, and curvature (Bishop andMinansy, 2006). Secondary terrain attributes are calculatedfrom a combination of two or more primary terrain attributesto model spatial variation of processes across a landscape, themost commonly used being the ‘topographic wetness index,’which is defined by Moore et al. (1991) as the natural

logarithm of specific catchment area divided by the tangent ofthe slope, and another being the SAGA wetness index (Olayaand Conrad, 2009).

Other methods that show promise for mapping soil moistureover large areas include airborne and spaceborne remote sensingby passive microwave radiometry or active radar instruments.However, both methods are highly sensitive to surface roughnessand their effectiveness is limited to flat and bare ground studies(Jonard et al., 2011; Kseneman et al., 2012), despite the advan-tage that they are not influenced by cloud cover.

Ground-based versions of these sensors (ground penetratingradar and L-band radiometer) are required for site-specific irri-gation management, and these sensors are available, and havebeen tested on a vehicle for mapping soil moisture in a field.Differences observed between the two methods were related todifferent sensitivities to surface roughness, and different ex-ploration depths; these technologies require further develop-ment before becoming commercially viable. The sensor datawere calibrated against TDR-derived soil moisture measure-ments at each position, and 20% of the reference TDR data wasrequired to produce a good roughness calibration model for theentire field, to correct the sensor data.

Other methods for mapping spatial heterogeneity of soilmoisture include electrical resistivity tomography (Kelly et al.,2011). While a strength of this method is its excellent verticalresolution, a weakness is that electrode arrays are inserted intothe ground for imaging and the method cannot be mobilized.However, Kelly et al. (2011) used it to delineate zones of ex-cessive water loss due to deep drainage, and this technology istherefore an advanced irrigation management technology.Kelly et al. (2011) used the information to position moni-toring sensors to assist irrigation scheduling.

Soil moisture mapping aids advanced irrigation schedulingbecause this scheduling directly measures the amount of


Web server

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Figure 13 Schematic diagram for a wireless sensor network (WSN) suitable for irrigation control. Adapted from Ruiz-Garcia, L., Lunadei, L.,Barreiro, P., Robla, J.I., 2009. A review of wireless sensor technologies and applications in agriculture and food industry: State of the art andcurrent trends. Sensors 9, 4728–4750.


available water left in a soil profile at any one time, and therate at which it is being used by the crop. It therefore directlymonitors crop water use (Thompson et al., 2007) and soilwater storage under any irrigation system, and provides in-formation in a digital form and can be used for open-loop orclosed-loop decision support tools.

Recent technological advances in WSNs provide the tool forreal-time high-resolution soil moisture monitoring withineach management zone defined by the EM survey so that soilmoisture maps can be updated on a daily basis and used eitherfor direct control of irrigation systems or for informing landmanagers.

Wireless Sensor Networks

The high spatial resolution provided by GPS-enabled sensingmethods (Section Advances in Irrigation Scheduling) can befurther refined by smart WSN technologies, and these tech-nologies are rapidly developing from off-line sensors usingfield loggers with manual downloading to wireless on-linesensor networks, within interoperable and autonomoussensor webs (see Figure 13). The sensor web concept is basedon the SWE framework of the OGC. Within this framework,standard protocols, interfaces and web services to discover,task, exchange, and process data from different sensorsand sensor networks have been defined (Thessler et al., 2011).Irrigation benefits from the resulting high temporal measuringresolution with real-time data transfer from spatially opti-mized management zones, and spatiotemporal models can beproduced to update static maps, on a daily basis, for improvedirrigation scheduling (Hedley et al., 2013).

These recent innovations in low-voltage sensor and wirelesstechnologies combined with advances in internet and cellularcommunication technologies offer opportunities for develop-ment and application of real-time management systems foragriculture (Evans et al., 2012; Pierce and Elliott, 2008;O’Shaughnessy and Evett, 2010; Coates and Delwiche, 2009).

Ruiz-Garcia et al. (2009) reported that wireless sensortechnologies are entering a new phase as a consequence ofdecreasing costs, increasingly smaller sensing devices, andachievements in frequency technology and digital circuits.Nodes, each with sensors attached, wirelessly form the mostefficient communication network to send data to a base sta-tion where they are stored and can be accessed remotely.

Alternatively, the data are transmitted via internet or cellularmeans to a secure database for storage, manipulation, andinforming irrigation schedules. Owing to a large number ofsensors and differing accompanying protocols, a coherent in-frastructure is required to treat sensors in an interoperable,platform-independent and uniform way. Standardized accessto sensor observations and sensor metadata provided by theOGC compliant Sensor Observation Service (Broring et al.,2011) acts as a mediator between a client and a sensor dataarchive or a real-time sensor system.

WSN technologies have significant potential to monitor in-herent soil variability present in fields with more accuracy thanexisting systems. Thus, the benefit for the producers is a betterdecision support system that allows maximized productivitywhile saving water. Installation of WSNs is easier than theexisting wired systems and sensors can be more densely de-ployed to provide local, detailed data; rather than irrigating anentire field in response to broad sensor data, each section couldbe activated based on local sensors.

Vellidis et al. (2008) developed a prototype of smart sensorarray for scheduling irrigation in cotton. The system integratesmoisture sensors, thermocouples, and radio frequency identi-fication (RFID) tags. Qian et al. (2007) designed a newgroundwater-monitoring instrument based on WSN thatmonitors groundwater table and temperature through a sen-sor. An embedded single chip processes the monitoring dataand a GSM data module transfers the data wirelessly. Bogenaet al. (2007) evaluated a low-cost soil water content sensor in awireless network application, and Kim et al. (2009) developedan in-field WSN for implementing site-specific irrigation con-trol in a linear move irrigation system. Communication signalsfrom the sensor network and irrigation controller to the basestation were successfully interfaced using low-cost bluetoothwireless radio communication. Hedley et al. (2013) used awireless soil moisture sensor network optimally positionedinto EM-defined management zones to inform a precision ir-rigation scheduling tool (Figure 14). The WSN monitored soilmoisture and depth to water table, the latter providing ameans of calculating the contribution of a high water table forsubirrigating the crop.

Underground systems for monitoring soil conditions, such aswater and mineral content, to provide data for appropriate irri-gation and fertilization are emerging (Akyildiz and Stuntebeck,2006) and these systems can also be used for monitoring the


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Figure 14 Flowchart to show WSN for precision irrigation scheduling. Reproduced from Hedley, C.B., Roudier, P., Yule, I.J., Ekanayake, J.,Bradbury, S., 2013. Soil water status and water table modelling using electromagnetic surveys for precision irrigation scheduling. Geoderma 199,22–29.


presence and concentration of various toxic substances in soilsnear rivers and aquifers, where chemical runoff could con-taminate drinking water supplies. Another application can belandslide prediction by monitoring soil movement.

Further recent WSN technological advances for irrigationscheduling include

• energy-efficiency gains using adaptive decentralized reclus-tering protocols for node communications (Bajaber andAwan, 2011; Nesa Sudha et al., 2011);

• algorithm development for handling orphaned nodes foroptimal restoration into the network (Maheswararajahet al., 2011);

• inclusion of wireless lysimeters for real-time online soildrainage monitoring (Kim et al., 2011); and

• incorporation of bluetooth and RFID technologies forautomated data capture and identification applications(Kim et al., 2009; Ruiz-Garcia et al., 2009).

Precision Irrigation Management

Available water supplies for irrigation are becoming increas-ingly limited globally and this will force major changes to the

design and management of water delivery for on-farm irri-gation management, as discussed elsewhere in the article(Section Simulation). Section Simulation discusses a technol-ogy that will potentially play an important role in future irri-gation management of limited water supplies: site-specificvariable-rate sprinkler irrigation or ‘precision irrigation’modification of self-propelled center-pivot and linear-movesystems (Hedley and Yule, 2009; Evans and King, 2012) (seeFigure 15). These systems are particularly suited to site-specificmanagement approaches because of their current level ofautomation and large area coverage with a single lateral pipe.Where sprinklers are modified for site-specific control, newopportunities arise to conserve water, reduce plant stress atlocalized positions, and reduce nutrient leaching and drainage.

Trials in New Zealand have shown that water savings aretypically between 10% and 25% where variable soils occurunder one system, and further savings are made by excludingirrigation from tracks, waterways, yards, sheds, and other un-productive areas (Hedley and Yule, 2011).

Management systems being developed alongside theseprecision irrigation systems include EM mapping to derivemanagement zones with real-time soil moisture monitoringwithin each zone. The Valley VRI system uses CropMetrics, asystem that derives EM and landscape change layer to identify



water-holding capacity variability across the field. These datalayers are delivered through a ‘Virtual Agronomist,’ where thedegree of field variability is used to decide on irrigationmanagement strategies. The amount of variability relates to theamount of opportunity present, that is, the higher the vari-ability the greater the opportunity for variable rate to benefit.Varying application rates increase input efficiency and improveyield production.

Findings from a modeling study by Hedley and Yule (2009)at five case-study sites in New Zealand found that where soilavailable water-holding capacity varied by 50 mm under oneirrigation system then the potential water savings were ap-proximately 10%, and variation by 4100 mm gave a potentialwater saving of Z15%. Savings are potentially greater in humidtemperate regions (where some rainfall occurs during the irri-gation season) in comparison with arid regions, where the mainbenefit of VRI for variable soils is a staggered start to irrigation atthe beginning of the irrigation season, plus different wateringstrategies for soils of contrasting textural and drainage prop-erties. Research has also been conducted to introduce wirelesssoil moisture sensor networks into EM and landscape-derivedmanagement zones for provision of real-time digital soil

Figure 15 Variable rate irrigator, with sprinklers switched off as theirrigator crosses a farm track, saves water and reduces lameness riskin dairy cows. Photo: C.B. Hedley.

Table 1 Spatial scales of common irrigation systems

System Spatial unit

Surface – furrow Single furrowSurface – furrow Set of furrowsSurface – bay BaySprinkler – solid set Wetted area of single sprCenter pivot, lateral move Wetted area of single sprLEPAa – bubbler Furrow dykeTraveling irrigator Wetted area of single sprDrip Wetted area of an emitteMicrospray Wetted area of a single s

aAbbreviation: LEPA, low-energy precision application.Source: Smith, R.J., Raine, S.R., McCarthy, A.C., Hancock, N.H., 2009. Managing spatial andof Multi-Disciplinary Engineering 7 (1), 79−90.

moisture information to the VRI controller. VRI control is es-tablished on-site or remotely through a software package withinternet or cellular connection.

Smart phone applications are being derived for irrigationcontrol and management, which is often more suited to op-erational farmer use, than a computer sitting back in the farmoffice. The WaterBee system has been developed in Europeindependently from a VRI system, and is the result of a projectundertaken by a team of 10 partners from eight Europeancountries targeting a sustainable solution to contribute to re-ducing freshwater use by the agricultural sector. WSNs sendreadings to a soil-moisture model that automatically adaptsirrigation requirements to different irrigation installations, andit is suggested that this WaterBee system will achieve real watersavings while enhancing crop quality.

The importance of scale in precision irrigationOne of the most important areas that may be ignored oroversimplified in precision irrigation is in choosing an ap-propriate scale at which variable rate (or other) technologiesshould be implemented. It is quite feasible to map spatial soilvariabilities and crop canopy differences at high resolution,and to engineer an application system to apply water variably.However, it is much more difficult to explain scientifically thereasons for underlying heterogeneity in crop growth and linkthis with confidence to decisions on variable water appli-cation. Deciding on the appropriate scale for applying waterneeds to be informed by a thorough understanding of theconsequences of soil and crop variability on yield. Advances inprecision agriculture thus need to be integrated with equiva-lent knowledge in precision irrigation to identify the appro-priate scale(s) for system implementation. Although it may betechnically and practically possible to apply water variably, itmay not be economically beneficial or agronomically sensible(see Table 1).

Other Advanced Irrigation Developments

Section Overview

This section provides an introduction to a selection of otheradvanced irrigation technologies including the modernization

Order of magnitude of spatial scale (m2)

100050 00010 000–50 000

inkler 100inkler 100

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temporal variability in irrigated agriculture through adaptive control, Australian Journal



of irrigation district networks and the use of smart meters toimprove the monitoring of water usage at the farm and fieldlevels. Selected on-farm irrigation technologies to improvecrop water productivity using partial root zone drying (PRD)and deficit irrigation strategies, vision sensing of crop re-sponses for irrigation management, and the application offertigation and chemigation are also discussed.

This section covers the following aspects:

• modernization of irrigation district networks,

• smart water metering,

• PRD and RDI,

• applied machine vision of plants for irrigation manage-ment, and

• fertigation and chemigation.

Modernization of Irrigation District Networks

The modernization of irrigation districts often involves con-verting the water distribution network from intermittent to‘on-demand’ water supply for farmers. On-demand irrigationschemes supply water to the farm via either gravity-fed chan-nels or pressurized pipe networks. The conveyance efficiency ofpressurized pipe networks are normally significantly greater(of order 30%) than for channel systems. Pressurized water atthe farm gate also provides farmers with an incentive to con-vert existing on-farm surface irrigation systems to potentiallymore efficient sprinkler or microirrigation systems.

The change from intermittent to on-demand water supply hasimplications not only for water use efficiency but also for irri-gated crop productivity and energy usage. For example, althoughmodernization of irrigation districts in Southern Spain has re-duced the amount of water diverted to farms for irrigation,consumptive water use has increased, mainly due to a change incrop rotation (Rodriguez-Diaz et al., 2011). However, in this area,the costs for system operation and maintenance have increaseddramatically (B400%), primarily due to increasing energy con-sumption for pumping (Rodriguez-Diaz et al., 2011).

In Southern Australia, Jackson et al. (2010) found thatconversion to pressurized irrigation methods reduced energyconsumption in regions where groundwater is used, the resultof an increase in efficiency of water use. They also suggestedthat conversion of on-demand gravity-fed systems into pres-surized networks is generally not appropriate where surfacewater supply is available. In these cases, regional investmentsshould focus on improving the volumetric efficiency of thechannel network and avoid increased energy requirements(Jackson et al., 2010). However, in the Harvey Irrigation dis-trict in Western Australia (Harvey Water, 2012), the avail-ability of elevated surface water dams close to lower elevationfarms has provided the opportunity to convert a channel dis-tribution system into a pressurized piped network that doesnot require pumping. Here, increased volumetric distributionefficiencies have provided water for alternative uses, whereasthe delivery of low-cost pressurized water on farm has enabledthe conversion of irrigation application systems and the es-tablishment of higher value horticultural crops.

The service performance of water supply schemes is afunction of the scheme and component capacities as well as theirrigation demand. Perez-Urrestarazu et al. (2009) observed that

on-demand systems should be designed to deliver water withflow rates and pressures required by on-farm irrigation systems,taking into account the time, duration, and frequency as de-fined by the farmers. However, due to the probabilistic natureof users irrigating simultaneously (Anwar et al., 2006) thesesystems are often designed with excessive distribution capacity,making them more expensive than intermittent systems(Planells-Alandi et al., 2001). Similarly, pump-pressurized, on-demand systems are commonly designed and operated tosupply the target pressure in each component of the pipe net-work irrespective of the water supplied. Sectoring, where farmersare organized to use water in turns, has been shown (CarrilloCobo et al., 2011) to be one of the most effective methods ofreducing energy consumption in pressurized, on-demand irri-gation networks. Computational tools involving integratedgeographic information systems, real-time monitoring, andmodeling of hydraulic data with decision support systems arealso being used (e.g., Perez-Urrestarazu et al., 2012) to improvethe operational performance of pressured irrigation networks.

An alternative approach has been taken in the modern-ization of the open-channel delivery systems in SouthernAustralia. Here the decision was made to retain the openearthen channels and to seek the efficiency gains throughautomation along with rationalization of the network in-volving retirement of some smaller channels and some limitedgravity pipelining and channel lining to reduce seepage losses.The classical ideas from system identification and control areused to automate the channels to provide a near on-demandsystem (Mareels et al., 2003, 2005; Cantoni et al., 2007). Thissystem has been implemented under the name of TotalChannel Controls and has resulted in distribution efficienciesin excess of 90% compared with the efficiencies of 70–75%typically achieved under manual control (see Figure 16).

Smart Water Metering

Knowing the amount of water being used and where it is usedare important elements associated with practicing efficient ir-rigation. Typical pressurized irrigation farms are characterizedby complex hydraulics due to numerous pipe fixtures andmodifications that occur over time, and variable irrigationblock flow delivery due to poor design and setup. Where flowmonitoring occurs, it is often conducted by manual readings ofa water meter at irregular intervals.

Smart irrigation metering involves the assessment ofunique hydraulic characteristics at the source of a deliverysystem with multiple outlets (Pezzaniti, 2009). This requiresan ability to record and automate analysis of high-frequencyflow and pressure sensor data and allows not only for thecontinuous monitoring of water consumption but also for theidentification of individual irrigation valve operation. Smartwater meters have the following attributes (Giurco et al.,2008): real-time monitoring, high-resolution interval metering(Z10 s), automated data transfer (e.g., drive by, GPRS, and3G), and access to data via the internet or SMS. Most modernmechanical and electronic water meters and pressure sensorshave features (e.g., pulse output) that allow flow to bemonitored or logged. Hence, the implementation of smartwater meters for monitoring on-farm irrigation typically

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Figure 16 Modernization of open-channel delivery in Australia provides a near on-demand system. Photos: Michael Kai courtesy Rubicon Water.


involves the addition of a datalogger and communications to atraditional water meter and pressure sensor.

Coupling the identification of valve operation with themeasured meter flows makes it possible to disaggregate thewater flow so that any component within an irrigation systemcan be identified. This enables the flow and total volume ap-plied to each irrigation block within the system to be recorded,providing comparative data for both the assessment of irri-gation efficiency and the identification of maintenance andoperating issues (e.g., pump wear, filter blockages, pipelineleaks, and emitter variations). The water use information ob-tained may be used to improve irrigation design and practice.Similarly, the subsequent analysis of smart water meter datacan be automated and integrated with controllers to optimizewater, energy, and maintenance requirements.

Partial Root Zone Drying and Regulated Deficit Irrigation

PRD and RDI strategies involve manipulating the placementof irrigation water and moisture deficit within the root zoneto increase crop water use efficiency. The major differencesbetween PRD and RDI are associated with the nature of thelocalized soil moisture and plant water status conditions(Ruiz-Sanchez et al., 2010). Both hydraulic and biochemicalsignals are involved in regulating stomatal and plant growthrates in response to changes in the abiotic environment(Chalmers et al., 1981; Davies and Zhang, 1991). PRD in-volves creating alternate drying and wetting of subsections ofthe plant root zone (Figure 17) to elevate biochemical

signaling while maintaining plant water status (Loveys et al.,2000; Stoll et al., 2000; Dodd et al., 2006). Hence, PRDstrategies maintain plant water status and create a favorablephysiological response through elevated biochemical signal-ing. RDI involves reducing the moisture availability through-out the entire plant root zone resulting in a reduced plantwater status (Kriedemann and Goodwin, 2003). RDI improvescrop WUE by maintaining plant water status within the pre-scribed limits of deficit with respect to maximum water po-tential (Kriedemann and Goodwin, 2003).

PRD strategies attempt to maintain water availability andplant water status simultaneously while elevating the bio-chemical signaling (increasing ABA levels and alkalization ofsap pH) within the plant. The elevated ABA has been found(Loveys et al., 2000; Stoll et al., 2000) to coincide with a partialreduction in stomatal conductance and a differential effect onvegetative and reproductive production (Davies et al., 2000),both of which lead to an improvement in crop water use ef-ficiency for fruiting crops.

Practical limitations in the successful application of PRDand RDI are related to the soil hydraulic properties, volume,and frequency of irrigation water applications, and the oc-currence of in-season rainfall. PRD and RDI strategies aredifficult to apply in furrow irrigation systems and PRD is alsodifficult to implement under sprinkler irrigation systems.However, both PRD and RDI may be implemented using dripirrigation and precision applicators on large mobile irrigationmachines. White and Raine (2009) suggested that the creationof a soil moisture gradient across the plant root zone largeenough to trigger a PRD response is most likely to be achieved


Figure 17 PRD in grapevines. Subsurface drip lines supply water to one side or the other of the vine. In this diagram water is supplied throughtwo irrigation cycles to the right hand line. The water content of the soil at various depths is shown as output from Enviroscan sensors. Althoughthe soil around the right hand sensor wets and dries in response to the irrigation, the soil on the left hand side of the vine continues to dry.Reproduced from Loveys, B.R., Grant, W.J.R., Dry, P.R., McCarthy, M.G., 1997. Progress in the development of partial root-zone drying. AustralianGrapegrower and Winemaker 403, 18–20.


on light-textured soils located in semiarid regions that ex-perience minimal in-season rainfall events.

RDI is particularly useful in controlling vegetative growthand increasing fruiting in indeterminate crops (e.g., cotton).For example, White (2006) found RDI (79% of predicted ET)of cotton under field conditions produced a 31.5% improve-ment in crop water use productivity over commercial practice(i.e., applying 100% of predicted ET). However, the largestbenefits derived from deficit irrigation were associated with themanagement of crop agronomy (i.e., vegetative growth, fruitretention rate, and crop earliness) and the increased utilizationof in-season rainfall.

Applied Machine Vision of Plants for Irrigation Management

The automated visual assessment of plant condition, specific-ally foliage wilting, reflectance, and growth parameters, usingmachine vision has potential use as input for real-time VRI andfertigation systems in precision agriculture. Crop-sensing tasksthat have been successfully demonstrated using machinevision in outdoor conditions include automated identificationof weed species (Slaughter et al., 2008), nitrogen status (Noh

et al., 2005), plant size (Shrestha and Steward, 2005), andmultispectral properties using narrow band imaging (e.g.,Carter and Miller, 1994). McCarthy et al. (2010) have reviewedthe use of applied machine vision for plant sensing in irri-gation applications.

Farm managers typically include visual assessment of cropcondition to inform management decisions (e.g., irrigationtiming) and treat the whole field uniformly based on theirmanual observations. For example, internode length measure-ment (i.e., the distance between branch junctions, Figure 18) ispart of a plant-based water stress monitoring regime for cottonsuggested for growers (Milroy et al., 2002). A machine visionsystem with access to a large proportion of the field potentiallyenables automatic condition assessment for different plants athigh spatial frequency in the field. Such sensing capability, inconjunction with the implementation of appropriate variable-rate application hardware, enables agricultural fields to betreated as a conglomerate of control units for operations such asirrigation and fertigation (e.g., Smith et al., 2009).

The design of a vision system for the measurement of plantattributes is affected by many factors, including the scale of theplant measurement (i.e., leaf- or canopy-level) and the meas-urement environment (e.g., a laboratory or in the field).


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Figure 18 (a) Moving image capture apparatus; and (b) stylized sample image from apparatus, with main stem nodes numbered. Reproducedfrom McCarthy, C.L., Hancock, N.H., Raine, S.R., 2009. Automated internode length measurement of cotton plants under field conditions.Transactions of the ASABE 52 (6), 2093–2103; Cotton plant graphic adapted from University of Hamburg, 1998. Virtual Plants. Available at: http://www.biologie.uni-hamburg.de/b-online/virtualplants/ipivp.html (accessed 19.10.12).


Automated machine vision sensing of individual plants in thefield is presently limited to early stage crops (where neigh-boring plants are too small to touch or overlap), or, for moremature canopies, to whole-plant characteristics such as plantbiomass. The use of near-infrared (NIR) imaging, backgroundboards, and shade structures with artificial illumination re-duces the complexity of the segmentation process but addsextra components and potentially physical bulk to the overallmeasurement system. In the indoor environment, a mon-ocular vision system can identify small canopy changes forirrigation scheduling purposes.

Identification of plant structure using stereo vision enjoysgreater success for smaller plants. Applications in the outdoorenvironment typically provide overall canopy geometry, whichis useful for monitoring crop growth in areas of a field oridentifying plant height changes, for example, between dif-ferent species (i.e., weed and crop). Determination of leafand branching structure of individual plants is currently lim-ited, even in indoor environments, and relies on the imagehaving a plain background. Knowledge of plant growthpatterns (e.g., phyllotaxis) potentially assists measurement byimage analysis.

The sensing and image analysis task may be simplified byimaging only in that part of the electromagnetic spectrum thataccentuates features of interest more effectively than the broadvisible bands provided by standard RGB cameras. Sensing ofdifferent regions of the electromagnetic spectrum potentiallyenables discrimination of plant materials based on color(visible), cellular structure (NIR), thermal (mid-infrared), orhardness (X-ray) properties.

Machine vision systems for field use must be designed to berobust to sunlight variations (Slaughter et al., 2008). Activesensing systems are less susceptible to ambient sunlight thanpassive sensing systems. However, low-cost (passive sensor)cameras with simple imposed illumination may also have re-duced dependency on sunlight (e.g., Edan et al., 2000).

Attaching a machine vision system to the gantry of a centerpivot or lateral move irrigation machine potentially enablescrop condition to be measured in real-time as the irrigationmachine moves across the field (e.g., Colaizzi et al., 2003;McCarthy et al., 2009). Alternatively, tractor-mounting of thesystem may be desirable, so assessments can be made as thetractor moves alongside the field. On-the-go in-field sensing ofgeometric crop plant parameters is currently limited to leafshape identification and biomass estimation in the foliage ofsmall plants, or plant height and biomass estimation in fullydeveloped canopies. To measure plant leaf-level attributes(e.g., internode length and leaf shape) in maturing field plantsrequires the design of a robust outdoor machine vision systemthat achieves a detailed structure sensing. These systems haveso far only been reported for automated laboratory or green-house systems on a limited number of crops under controlledlighting and environmental conditions.

Fertigation and chemigation

Efficient nutrient and chemical use in agriculture involves ac-curate spatial and temporal placement of the applied fertilizeror chemical. Fertigation involves supplying dissolved fertilizerto crops through an irrigation system (Bar-Yosef, 1999). Al-though fertigation has the advantage of being able to accur-ately apply fertilizer, liquid application of ammoniacalfertilizers can lead to deleterious drops in soil pH (Stork et al.,2003), because roots release hydrogen ions to take up am-monium ions, and this should be considered when fertigatingwith these compounds. Fertigation is commonly applied usingboth surface and pressurized irrigation application systems.When combined with an efficient irrigation system, both nu-trients and water can be manipulated and managed to maxi-mize marketable yield and nutrient efficiency (New SouthWales Dept of Primary Industries (NSW DPI), 2000). Soluble


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inorganic nutrients are normally used for fertigation but theapplication of humic substances via fertigation systems hasalso been found (Selim and Mosa, 2012) to increase root zonemoisture holding capacity in sandy soils and increase cropproductivity.

Chemigation in broad-acre crops is most commonly ap-plied by large mobile irrigation machines (i.e., center pivots orlinear moves). Herbicides, insecticides, and fungicides may beinjected into the main irrigation water pipe for distributionthrough the irrigation emitters (e.g., Quad-Spray, SenningerIrrigation Inc., Clermont, FL, USA) with the water. Alter-natively, chemigation may be conducted using a separatesystem of distribution pipework with spray heads suspendedunderneath the irrigation machine truss rods to enable theapplication of chemical either with or without irrigation water(Foley and Raine, 2001).

The efficiency of fertigation and chemigation strategies ishighly dependent on the performance (i.e., uniformity) andmanagement of the irrigation system. Fertigation uniformity inmicroirrigation systems is primarily a function of emitter dis-charge uniformity where the fertilizer is injected during themiddle third or half of the total irrigation time (Hanson et al.,2006). Fertilizers injected into water applied to furrowirrigation systems are similarly affected by the nonuniformityof the applied water, with efficiencies often low due to per-colation losses at the head end of the field and tailwater runofflosses. Simulation models combining the overland waterflow (Saint-Venant equations), solute transport (advection–dispersion), and infiltration have been developed (Perea et al.,2010; Burguete et al., 2009) to evaluate and optimize the ap-plication of fertilizers in furrow irrigation systems. Modifyingthe water inflow hydrograph to improve the uniformity ofwater application and reducing tailwater runoff has beenfound to improve both nutrient uniformity and efficiency(Moravejalahkami et al., 2012). Similarly, using alternativefurrow irrigation strategies has also been found (Ebrahimianet al., 2012) to increase lateral water movement, reducingwater and nitrate losses via runoff and deep percolation.

Decision support systems are increasingly being used (e.g.,Incocci et al., 2012) to identify optimal fertigation require-ments based on crop growth and environmental conditions.Site-specific fertigation of zones within conventional pressur-ized irrigation systems may be achieved by the installation andcontrol of separate injection facilities for each zone. However,implementation of separate systems to date has been limitedbecause of the expense and control complexity (Coates et al.,2012). Where a centralized injection facility is used, the se-lection of the optimum injection strategy will be a function ofthe crop needs, scheduling limitations, and system designparameters including emitter type, fluid distribution systemtravel time, and field slope (Coates et al., 2012).

Future Directions – Emerging Risks, TechnicalChallenges, and Future Developments

Section Overview

Despite concerns regarding international food security, and thedrive to support sustainable intensification, agriculture still

faces a number of challenges that are likely to hamper anywidespread uptake of advanced technologies, including pre-cision irrigation. This section identifies the emerging risks and‘drivers for change,’ highlights the environmental and techni-cal challenges constraining innovation in precision irrigation,and briefly considers selected novel technologies on the hori-zon that will support the future sustainability of irrigatedagriculture and horticulture.

Emerging Risks

In most countries, agriculture provides significant societalbenefits, by making important contributions to nationaleconomies and underpinning rural employment. Although themost obvious contribution is probably in the productionof ‘food’ and ‘nonfood’ crops, agricultural ecosystems alsoprovide other services, including regulation of air quality, cli-mate, and water purification. Agricultural land delivers non-material cultural benefits such as land for recreation andvalued characteristic landscapes, supporting habitats, wildlife,biodiversity, and ecosystem services. The importance of agri-cultural land, including irrigated croplands, therefore goes farbeyond food production – the future actions of farmers canthus have positive or negative effects on these services, all ofwhich are likely to be affected by climate change.

Climate impacts on irrigated productionInternationally, agriculture is regarded as one of the sectors atmost risk from a changing climate, due to the impact of in-creased temperatures, reduced rainfall, and increased fre-quency of extreme events, not only in the tropics but also inhumid and temperate environments (Falloon and Betts, 2010;Knox et al., 2012). Outdoor rainfed and irrigated crops areparticularly sensitive, both directly from changes in rainfalland temperature and also indirectly, as any change in climatewill also impact on the agricultural potential of soils bymodifying soil water balances and changing land suitabilityfor production (Daccache et al., 2012). These changes will inturn affect the availability of water to plants and impact onother land management practices (e.g., trafficability for seed-bed preparation, spraying, and harvesting) including the de-mand for irrigation (Daccache et al., 2011). In regions whererainfed agriculture is dominant, changes in the timing, distri-bution, and reliability of rainfall may force a gradual switch toirrigated production, to maintain crop yields. Here precisionirrigation could become important, particularly for sup-plemental irrigation. By combining better weather forecastingtechniques to make better use of effective rainfall with, forexample, VRI, the negative impacts of agroclimate uncertaintyon crop yield and quality could be reduced.

Farmers also face a range of ‘nonclimate’ risks that poten-tially represent a more immediate threat to sustainable foodproduction than climate change. Most notable is the increasingburden of environmental protection and its consequent im-pacts on water resources (both supply and allocation) forirrigated agriculture (Knox et al., 2010). However, investmentin advanced precision irrigation technologies still requiresconsistent and reliable supplies of water.

Figure 19 Overhead irrigation on iceberg lettuces using a modifiedmobile hosereel fitted with a boom. Before harvest, the sprinklers arereplaced with drop tubes to avoid soil splash impacting on cropquality. Photo: J. Knox.


Demands for greater environmental protectionGovernments and society are seeking greater levels of en-vironmental protection. In irrigated agriculture, probably thegreatest short-term risks relate to the impacts of new waterregulation. For example, in Europe, a number of new directiveshave recently been enforced, including the Water FrameworkDirective (WFD 2000/60/E). Despite its title, the WFD is asmuch about land management as it is about water manage-ment. It is the most substantial legislation produced by theEuropean Commission and provides the major driver forsustainable management of water across Europe. By 2015, itrequires that all inland and coastal waters within defined riverbasins reach at least ‘good status’ and defines how this shouldbe achieved through the establishment of environmental ob-jectives and ecological targets for surface waters. The WFDapplies to all waters to tackle diffuse source pollution, rangingfrom fertilizer and pesticide applications on rainfed and irri-gated land to urban runoff. In agriculture, the dependence onfertilizers and pesticides means that many farms may be sub-jected to much greater levels of surveillance to ensure thatdiffuse pollution from cropped areas is not contributingto water quality degradation. Irrigated agriculture is widelyviewed as a key target for improvement. Similar pressures onirrigated farming are known to exist in other continents in-cluding the US and Australasia.

However, the rising costs for fertilizer (and energy for waterpumping) are themselves acting as an industry brake, withmany irrigated farms actively seeking new measures to reducefertilizer inputs and water use. Collectively, these may providea positive indirect response to water regulation and drive theuptake of precision irrigation technologies. These could helpreduce nitrate leaching risks, nonbeneficial losses of water off-farm, and levels of energy consumption (and hence carbonfootprint) for irrigated production.

Although most environmental regulations are imple-mented at the river basin or catchment scale, their impact willbe felt at the farm level, particularly when irrigated farms arelocated in water-stressed catchments or in proximity to inter-nationally protected habitats or environmentally designatedsites. Under these conditions, precision agriculture practices,including irrigation, could again help reduce some of theimpacts of agricultural water abstraction on local habitats andthe risks associated with nitrate leaching to the environment.

Finally, farms in the future may be subjected to increasinglevels of monitoring and scrutiny (traceability) to demonstratecompliance with national and international regulation. Pre-cision irrigation technologies will undoubtedly have an in-creasing role in demonstrating ‘best practice’ in irrigationmanagement. In this context, technologies that target waterapplications both spatially and temporally, taking into ac-count heterogeneities in soil moisture, crop development, andclimate, are likely to be viewed positively by environmentalregulators. In parallel, changes in water regulation are exposingirrigated farms to new water supply risks.

Competition for water resourcesInternationally, irrigated agriculture faces rising competitionfor access to reliable, low-cost, and high-quality water. InNorthern Europe, for example, farmers are under increasingregulatory pressure to improve irrigation efficiency; indeed,

demonstrating ‘efficient’ water use is a prerequisite forrenewing an abstraction license (permit) (Knox et al., 2012). InMediterranean Europe, where irrigated agriculture accounts forapproximately 60% of all abstractions (OECD, 2012), pro-duction is at risk due to increasing water scarcity and com-petition for scarce resources (Wriedt et al., 2009). In somecountries, abstraction regimes are in place, but in others newframeworks for regulation, including those for irrigation con-trol are being implemented. Within these frameworks, higherlevels of water efficiency will inevitably be required. Precisionirrigation, often only considered in the form of drip (or trickle)irrigation, is often seen by water regulators as being ‘good’ forthe environment. Other forms of precision and VRI will nodoubt be encouraged.

In many catchments where irrigated agriculture is concen-trated, rising demand for water between different sectors(notably, agriculture, public/domestic supply, and industry)coupled with reduced allocations to meet environmental flowsmeans that allocations for irrigation are becoming less reliableand more expensive. The situation is exacerbated by exampleswhere irrigated agriculture is cited as being the primary causeof environmental damage and overabstraction, mainly duringthe summer months when river and groundwater levels are attheir lowest and irrigation demands peak.

In future, farmers will need to demonstrate more efficientand sustainable use of water to secure rights (licenses/permits)for irrigation abstraction. Technical measures such as switchingfrom sprinkler to micro (drip)-irrigation are often promoted byindustry and the regulator to reduce the environmental impactof abstractions and increase water efficiency. Replacement ofoverhead irrigation sprinklers with drop tubes is an example ofsystem modification to efficiently deliver irrigation to the rootzone of a salad crop, also minimizing soil splash onto the plant,to reduce the amount of washing required during the processingstage (Figure 19). However, any environmental gain may belimited if more efficient techniques such a precision irrigationdo not result in a reduction in net water use, but simply supportan increase in irrigation command. Any water ‘saved’ via pre-cision irrigation could be reallocated to other crops; the



environmental impact could thus be negative not positive, dueto reduced return flows from previously ‘inefficient’ irrigation(Hedley and Yule, 2008; Perry et al., 2009).

Drivers for Change

Development of innovative approaches to combine betterspatial and temporal knowledge of soil, crop, and equipmentmanagement practices to reduce variability in crop yield andquality through advanced irrigation technologies is a major‘driver for change,’ whether driven by consumer (market),regulatory, and public demands for greater environmentalsustainability.

To maintain output, agriculture has intensified and becomemuch more specialized, and for many farmers, investment inirrigation has provided the basis to maintain or increaseprofitability. For example, in high-value cropping, irrigation isnot a marginal activity used to boost yield, but an essentialcomponent of production to deliver premium quality, con-tinuous supplies of produce to processors and retailers. It hasalso become a prerequisite for meeting the increasing marketdemands for quality and continuity of supply. Despite thisexternal driver, irrigation of field crops in many parts of Eur-ope and elsewhere has changed relatively little over the last fewdecades. However, with rapidly rising labor and energy costs,farm businesses are now assessing the impacts of irrigationvariability (nonuniformity) on crop yield and quality muchmore proactively (see e.g., Figures 5, 6, 15 and 19). This isbecause the quality assurance benefits of irrigation can besubstantial and relate to the whole crop, not just to the extramarginal yield due to irrigation. Quality criteria are increas-ingly specified as a condition of contract and sale, and failureto meet quality requirements can lead to large price dis-counting, and possibly rejection and loss of contract.

Over the past decade, grower or crop assurance schemeshave also played an important part in driving water efficiencyin irrigation and supporting uptake of advanced irrigationtechnologies. These schemes require growers to audit their ir-rigation systems and provide traceability and accountability insupport of public health and environmental protection regu-lation. These schemes have also provided the basis for growersto comply with industry protocols for food safety and cropassurance (Monaghan and Hutchison, 2012).

In the future, water cost is likely to become a major driverfor change – not necessarily the unit cost for securing access toa water supply, but more likely the energy costs associatedwith conveyance and pressurized delivery of in-field irrigation.With increased attention to water conservation during droughtspells, competition from environmental, recreational, public/domestic use, and regulatory constraints, the current eco-nomics of VRI, which are proving a deterrent to investment,may well change (Sadler et al., 2005; Hedley and Yule, 2009).

Future Developments

Combining wireless technologies with variable rateapplicationOn most farms, making maximum use of soil moisture andrainfall, knowing precisely where and when irrigation has to

be applied, and then applying it accurately and uniformly arethe fundamental steps in the pathway to water efficiency(Knox et al., 2012). Although irrigation is an essential com-ponent of production to maximize yield in arid and semiaridregions, there is growing evidence that optimized irrigationregimes under temperate and humid conditions can also leadto improved postharvest quality resulting in reduced cropwaste through the food supply chain. However, at presentmost farmers are restricted in their ability to match the timingand frequency of irrigation applications to inherent spatial andtemporal variations in soil moisture and crop growth. Theygenerally have only limited information on plant water status,rely on limited in-situ point measurements of soil as a proxyfor field-scale soil moisture availability, and use conventionalirrigation systems that lack sufficient flexibility and technology(control) for variable water application.

However, developments in crop and soil moisture sensing,coupled with wireless telecommunications for in-field soilmoisture monitoring and thermal imaging, now provide op-portunities to develop smarter, closed-loop systems capable ofapplying water variably both across and along fields. Mostresearch to date has focused on developing such technologiesfor use under sprinkler (center pivot and linear move) irri-gation systems. The potential for VRI using individually con-trolled solenoid valved sprinklers, similar to those used in thesports turf (golf) industry, is also now being evaluated in highvalue horticulture, where sprinkler irrigation is still the pre-ferred method of application.

Digital advances in cloud computing and remote sensingAlongside innovations in irrigation systems and soil moisturemonitoring, digital advances using the latest cloud computingtechnologies are also moving swiftly into precision agriculture.Put simply, cloud computing involves using networks of re-mote servers hosted on the internet to store, manage, andprocess data, rather than hosting information and data onlocal servers. They generally rely on wireless data transfer andmobile web applications, in combination with other tools andspatial technologies including GPS and GIS. Cloud technologyis well established within data-intensive industries, but onlyrecently emerging in agriculture where various applications arebeing marketed. For example, in the USA, cloud services pro-vide on-farm support from agribusinesses and consultants, foragrochemical application management. Other precision-related tools are now emerging.

New uses relating to precision irrigation could includeapplications for mobile devices operating in the cloud tospatially monitor soil moisture, crop growth, and irrigation inreal-time via in-field sensor arrays. Other cloud uses includeproviding data to refine planting and harvest operations, byintegrating GPS and GIS data or managing equipment per-formance (pressures, flow rates, abstractions) at district orcatchment scales. RFID tags, which automatically downloaddata, are also becoming more widespread in agriculture. Forexample, tagging systems have been developed to collect dataon the moisture content of straw bales, weight, and in-fieldposition (GPS); in the future, similar cheap, possibly bio-degradable, microtags could be deployed across fields tomeasure seasonal changes in soil moisture, organic content,crop canopy development, and canopy stress, or for


monitoring and optimizing energy needs across pressurizedirrigation distribution networks (Carrillo Cobo et al., 2011).However, data security issues relating to confidentiality, in-tegrity, availability, and accountability still need to be resolvedbefore cloud technology can be fully integrated into precisionirrigation.

There is also increasing potential for new applicationslinking the use of high-resolution and -frequency remotesensing data (e.g., MODIS) to inform on-farm irrigationmanagement, including mapping croplands, and monitoringspatial changes in crop cover in support of farm monitoring ofirrigation water use and evapotranspiration (ET) (Thenkabailet al., 2012). Recent remote sensing developments providescope for mapping croplands in a routine, rapid, and con-sistent way, with sufficient accuracy (Congalton and Green,2009). There is also potential to use remote sensing to identifyirrigated regions, where improvements in water productivityshould be targeted to reduce ‘yield gaps’ (Fereres et al., 2011).By integrating advanced technologies such as cloud computingwith developments in precision irrigation and remote sensing,there is also broader scope to improve one’s understanding ofthe links between food production and water scarcity, and theimpacts of climate change on food supplies.

Summary

In the future, committed efforts will be needed to implementadvanced irrigation technologies that are appropriate for dif-ferent types of farming systems to improve both water andenergy efficiency while maintaining or improving crop yieldand quality. Considering the demands on natural resources,precision irrigation is likely to play an increasingly importantrole, but a number of factors remain important. These includethe changing economics of irrigation, the rising cost of energy,the increasing importance of crop assurance, and the role ofretailers (supermarkets) in influencing consumer attitudes andbehavior toward quality assurance and fresh produce.

The uptake of precision irrigation is likely to be slow anddependent on appropriate support systems and knowledgetransfer to engage farmer support and trust. Insufficient rec-ognition of field variability, the lack of a whole-farm approach,limited knowledge of the links between crop quality andprecision irrigation, and the alignment of crop assuranceschemes with environmental auditing will all need to beresolved.

The way forward for precision irrigation seems to mirrorobservations from precision agriculture. Here the key has beento keep the farmer’s perspective central to the objective. Farmerneeds, of course, vary depending on the agricultural system (e.g., intensive horticulture vs. extensive broad-acre cropping),the scale of business (e.g., family farm vs. agribusiness),underlying agroclimatic conditions (e.g., arid vs. humid), andmany other socioeconomic factors (e.g., attitudes to risk, etc.).The tacit knowledge of farmers is thus critical.

New farm-scale precision equipment assists farmers to fine-tune the existing management procedures, but requires theaccompanying decision support tools to monitor and adaptthis new level of control.

Such technologies can be carefully positioned to fill specific(and crucial) gaps in the existing irrigation toolkits using the

tacit knowledge of farmers (McBratney et al., 2005) to assistimproved farm-scale irrigation practice.

The limited opportunity to increase global freshwater al-locations for irrigation (OECD, 2012) will require collectiveinitiatives beyond the farm gate to optimize regional-leveltradeoffs. These include tradeoffs between improved water useefficiency of modern pressurized systems versus their greaterenergy consumption; as well as tradeoffs between the best useof irrigation to meet global food demands and the pressingneed to maintain the multiple ecosystem services that globalfreshwater resources provide.

See also: Climate Change: Agricultural Mitigation. Climate Changeand Plant Disease. Climate Change: Cropping System Changes andAdaptations. Climate Change, Society, and Agriculture: AnEconomic and Policy Perspective. Food Security: Food Defense andBiosecurity. Food Security, Market Processes, and the Role ofGovernment Policy. Food Security: Postharvest Losses. FoodSecurity: Yield Gap. Precision Agriculture: Irrigation. Virtual Waterand Water Footprint of Food Production and Processing. Water Use:Recycling and Desalination for Agriculture. Water: Water Quality andChallenges from Agriculture. World Water Supply and Use:Challenges for the Future

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