Waterlogging in Australian agricultural landscapes:a review of plant responses and crop models

Ruth E. ShawA,C, Wayne S. MeyerA, Ann McNeillB, and Stephen D. TyermanB

ASchool of Earth & Environmental Sciences, The University of Adelaide, Glen Osmond, SA 5064, Australia.BSchool of Agriculture Food and Wine, The University of Adelaide, Glen Osmond, SA 5064, Australia.CCorresponding author. Email: ruth.shaw@adelaide.edu.au

Abstract. This review summarises reported observations of the effects of waterlogging on agricultural production inAustralia and briefly discusses potential remediation strategies. Inconsistencies are demonstrated in the current indicatorsused for assessment of waterlogging potential across agricultural landscapes as well as in parameters measured inwaterlogging studies. It is suggested that predictions of waterlogging potential for landscapes should be based on aminimum dataset that includes pedological, topographical, and climate data for the defined area, as well as observations ofplant morphological appearance and visible surface water. The review also summarises the effects of low oxygenconcentration in soil on rhizosphere processes, and discusses evidence for direct effects on plant physiology ofreductions in soil oxygen caused by waterlogging. Finally, the review describes current crop growth, water use, andyield simulation models used in Australia (SWAGMAN, DRAINMOD, and APSIM) that incorporate waterloggingstress. It is suggested that there is scope for modifications to these models based on recent improved understanding ofplant physiological responses to waterlogging and on further research. The review concludes that improvements inmodelling waterlogging outcomes to assist growth and yield predictions should ultimately enhance managementcapacity for growers.

Additional keywords: raised bed, drainage, aquaporin, hydraulic conductivity, soil aeration, economics.

Received 4 March 2013, accepted 23 July 2013, published online 30 August 2013

Introduction

Waterlogging in Australia undoubtedly causes economic lossesin all areas of agricultural production, including cotton (Bangeet al. 2004; Reicosky et al. 1985), grains (Grieve et al. 1986;McFarlane et al. 1989), and pastures (Jerrems and Hill 1999).Indeed, potential economic losses in Australia due to effects ofwaterlogging have been reported to be AU$300million perannum for wheat alone (Dennis et al. 2000). However, there issubstantial uncertainty associated with these estimates of yieldloss due to differences in the way that areas affected bywaterlogging are defined. The assessments of areas affectedare often based on direct observations, such as surface water,or are extrapolated using parameters that are indirectly related tooccurrence of waterlogging, such as soil characteristics. Hence,different jurisdictions report assessments of waterlogging indifferent ways. For example in Western Australia’s grain belt,1.8Mha of soils are described as ‘periodically waterlogged’(Khabaz-Saberi et al. 2010), whereas in Victoria ~4Mha ofsoils are considered as having the ‘potential’ to cause cropyield loss as a result of waterlogging (Setter and Waters 2003).Yield loss due to waterlogging may be over- or under-estimated,since ‘susceptible areas’ are highly unlikely to be uniformly orequally affected, and saturated soils are not always apparentfrom surface evaluation.

There is also a scarcity of reported biophysical data inAustralia regarding crop responses to waterlogging, such aswilting or yellowing of leaves or reduced leaf and shootgrowth, and rarely have the same parameters been reportedacross studies. Furthermore, assessments focus on plantobservations only (Reicosky et al. 1985) or solely on soilparameters (Grieve et al. 1986; MacEwan et al. 1992; Davieset al. 2000; Fitzpatrick et al. 2003) and less commonly on thesoil–plant system (Reicosky et al. 1985; Grieve et al. 1986;McDonald and Gardner 1987; McFarlane et al. 1989;MacEwan et al. 1992; McDonald 1995; Jerrems and Hill1999; Davies et al. 2000; Bange et al. 2004; Milroy andBange 2013). Improved assessment may be possible throughthe use of simulation modelling that has the potential to assignyield reductions to particular stresses. The success of thisapproach will depend on the accuracy of the representation ofwaterlogging processes within the models. Common cropgrowth models used in Australia have limited representation ofplant physiological responses under waterlogged conditions.The models use rudimentary knowledge of plant responses towaterlogging that was formulated more than two decades ago,with little evidence of review in recent times. Indeed, the mostrecent Australian review of waterlogging, undertaken >20 yearsago,was limited to theMurrayValley region ofNewSouthWales

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and focussed solely on the soil system in that irrigated landscape(Grieve et al. 1986).

The condition of waterlogging is commonly described whenthere is obvious saturated soil and/ or water ponded on the soilsurface. Waterlogging can occur either where there is a risingwatertable or from continuous addition of water to the surface,for example during irrigation or high-intensity rainfall wheresoils have slow drainage. Reductions in crop growth and yieldfrom excess water in soil can be broadly attributed to three maininfluences that are discussed in this review: (i) the direct effect onplant physiological processes from low oxygen concentrationsin the soil around roots (rhizosphere); (ii) changes in soilchemistry and reduced nutrient availability resulting intoxicities of some elements and deficiencies of others; and(iii) changes in microbial activity affecting normal rhizosphereprocesses. All three are primarily a consequence of anoxicconditions in soil. Although salinisation of soils can occur inconjunction with waterlogging (Aubertin et al. 1968; West andTaylor 1979), this review focuses on effects of waterloggingper se on plants.

The review first summarises reported observations of theeffects of waterlogging on agricultural production in Australiaand briefly discusses potential remediation strategies. Currentindicators used in assessment of waterlogging potential acrossagricultural landscapes, as well as parameters measured in fieldstudies of waterlogging, are discussed. Second, the reviewcomments on the effects of low soil oxygen concentration onrhizosphere processes, along with evidence for the direct effectsof low oxygen on plant physiology caused by waterlogging,including a proposed link between changed aquaporin activity,a reduction in root hydraulic conductance, and leaf wilting.Third, the review discusses current crop growth, water use,and yield simulation models that can incorporate waterloggingstress, and the potential for modifications given recent improvedunderstanding of root water-uptake processes.

Occurrence and potential for waterlogging in Australia

When water from rainfall or irrigation exceeds the net watermovement out of the soil profile, soil pores will become water-filled, displacing soil air and becoming ‘waterlogged’.Waterlogging can occur in both irrigated and rainfed areas, butit is more prevalent in irrigated areas (Cannell 1977), wherethere is a greater chance of extra water from rain or additionalirrigation on already wet soils. Many irrigated areas developshallow ground water (Grieve et al. 1986), and consequently, thevolume of the unsaturated soil zone is less than in an equivalentrainfed area.

Waterlogging is common in duplex (or texture contrast) soils(Cannell 1977; Dracup et al. 1992; MacEwan et al. 1992;McFarlane and Cox 1992; Hatton et al. 2002; Setter andWaters 2003) due to poor drainage. Duplex soils have a highor moderately high hydraulically conductive soil layer above alow hydraulically conductive soil layer. The extreme example isa coarse sand layer over clay subsoil. Drainage through such asoil profile is controlled by the layer with the lowest hydraulicconductivity, often the clay layer. Duplex soils are widespreadin Australia, covering ~20% of the landscape (Chittleborough1992).These areas with duplex soils correspond closely to those

reported to be prone to waterlogging, as indicated by theoverlying circles depicted in Fig. 1.

Waterlogging effects on crop yield and economics

A comparison of the parameters measured during fieldobservations of waterlogging at locations around Australiaover the past two decades highlights the paucity of consistentdata, with no single measurement common to all studies,surprisingly, not even percentage yield reduction(Table 1). This lack of consistency makes it difficult togeneralise expected crop responses that could subsequently beobserved and linked to measured crop yield reduction. Thesummarised yield reduction and economic loss data(Table 2) are discussed in this section, along with availablepertinent information regarding soil type, rainfall, and plantmorphological observations.

Several common factors are recorded in field studies ofwaterlogging, including soil properties and rainfall events.Details of soil properties and of whether profiles are duplexare commonly reported (Grieve et al. 1986; McFarlane et al.1989; MacEwan et al. 1992; Siddique et al. 1993; Davies et al.2000; Milroy et al. 2009; Milroy and Bange 2013), although thedifferentiation between sloped or flat land is rarely recorded(McDonald 1995; Reicosky et al. 1985). Heavy rainfall(>360mm) during the winter months of June, July, andAugust or rainfall exceeding evapotranspiration (Grieve et al.1986; McDonald and Gardner 1987; McFarlane et al. 1989;Siddique et al. 1993; McDonald 1995; Davies et al. 2000) iscommonly recorded.

A limited number of studies discuss crop morphologicalresponses or observations due to the effects of waterlogging(Table 1). Leaf growth reduction and wilting have beenrecorded (Reicosky et al. 1985), but more commonly recordedis that waterlogging causes crop disease (McFarlane et al. 1989;Siddique et al. 1993); consequently, the disease and change innutrient status results in leaf chlorosis and necrosis (McDonald1995). Differences in waterlogging tolerances between differentspecies have been reported (Davies et al. 2000), as havedifferences even between closely related species; for example,yellow lupin (Lupinus luteus) tolerates waterlogging better thannarrow-leafed lupin (L. angustifolius). Crop yield reductions ashigh as 80% have been recorded (MacEwan et al. 1992), whilstannual economic losses up to $11million in the state of WesternAustralia have been reported (McFarlane et al. 1989).

This review of Australian field research into waterloggingover the last 20 years revealed that there are very fewphysiological criteria routinely used to assess waterlogging inthe field (Table 1). One advantage to measuring such parametersis that the information could be used to improve models thatsimulate effects of waterlogging (as discussed later), and thusimprove the capacity to better predict reduced crop growth andyield. Further, we suggest that predictions of the potential forwaterlogging in landscapes should be based on the followingminimum dataset:

* Soil profile information—proportion of the area underconsideration that has duplex soils;

* Topographical data—slope and proportion of the area on thedownside of slopes;

550 Crop & Pasture Science R. E. Shaw et al.

* Meteorological information—area of landscape and timeperiods when rainfall exceeded plant evapotranspiration;

* Timing of, and specific descriptions of, plant morphologicalappearance (e.g. wilting, yellowing) in areas with visiblesurface water;

* Extent of areas with visible surface water (e.g. surface waterestimated by remote sensing).

Consistent information as recommended above will givecomparable data of waterlogged sites, enabling betterprediction of reduced crop growth and yield, and henceimproved prevention or avoidance actions.

Current management practices to amelioratewaterlogging

Detrimental effects of waterlogging on yield may be reduced byincreased drainage. Surface drainage such as ditches, open drains,laser levelling, and raised beds are used to drain excesswater fromthe ground surface. Techniques to increase subsoil drainage mayconsist of interceptor drains, deep ripping to disrupt soil layersthat impede drainage, subsurface pipes (clay tile pipes, slottedplastic drains), mole drains, and gravel mole drains.

Raisedbeddrainage (or ridge tillage) describes a systemwhereplants aregrown in rowsonsoil that is shaped into ridges orhills ofvariouswidths.Often the furrow spacing is basedon tractorwheelwidths for ease of land management and to reduce soilcompaction over multiple paths. The intention is to have theupper soil profile drain freely both laterally and vertically and forwetting to be primarily through capillary rise rather than beingsaturated from a complete covering of surface water. The

expectation is that the soil in the raised beds will rarely besaturated, and hence will retain higher air and oxygenavailability. Tisdall and Hodgson (1990) reviewed raised beduse in Australia with examples from fruit orchards in northernVictoria and cotton and sugarcane in Queensland and New SouthWales. Yield increases from raised bed application comparedwith conventional practice were variable, but increases rangingfrom 25 to 50% were reported. Grain crops grown in WesternAustralia with raised beds compared with conventional practiceshowed an average increase of 18% over multiple crops andmultiple seasons (Bakker et al. 2005, 2007).

Several drainage techniques implemented around Australiahave been reviewed, including clay tile, pipe, and mole drains(MacEwan et al. 1992). It was concluded that drainage is a costlyand long-term solution, with difficulty in predicting yieldoutcomes before implementation. Mole drainage (unlined,horizontal holes 0.5–0.8m below the ground surface) installedat Barongarook, Victoria, increased pasture production in year 1by 72%, in year 2 by 17%, and in year 3 by 13%. This indicatesthat mole drainage has short-term effectiveness in these soils andmust be reinstalled tomaintain function.Although installingmoledrains can increase crop production, thewindowof opportunity toinstall stable drains in many soils is very limited. This hasrestricted widespread use as a means of improving profiledrainage.

Little information is available on the change in crop yield thatinterceptor and relief drains provide to waterlogged areas.Interceptor drains are cut across sloped land, with a soil bankon thedown-slope side tocollect subsurface andsurfacewater andprevent the water from reaching the protected or waterlogged

Fig. 1. Distribution of duplex soils in Australia (Chittleborough 1992), with areas where waterlogging hasbeen reported in the literature (circled).

Waterlogging in Australian landscapes Crop & Pasture Science 551

area. Relief drains are cut down a slope or on flat land to channelwater. The water intercepted from interceptor drains has beenused to predict crop response down-slope of the drains (Cox andNegus 1985). An increase in oat (Avena sativa L.) yields due tointerceptor drains was recorded and it was noted that drainspacing plays an important role in the efficiency of drains forinterceptingwater and therefore increasing cropyield (McFarlaneand Cox 1990).

Clearly, regardless of efforts to drain soils, there are situationswhere waterlogging will occur, and our ability to predict theimpact of these occurrences on crop yields will be improved byincreased understanding of soil–plant system responses towaterlogging. The following section presents an overview ofthe chemical and physical changes in soils that becomewaterlogged, as well as the plant physiological mechanismsand the morphological adaptations involved in response towaterlogging (Fig. 2).

Soil response to waterlogging

Soil chemistry and microbial activity change within the soilunder the low oxygen conditions of waterlogging. The processof decomposition of organic matter in waterlogged soils isdifferent from and slower than in aerated soils, resulting indifferent end products within the soil matrix. In well-aeratedsoil, the products from decomposition include carbon dioxide,nitrate, and sulfate, whereas in a waterlogged soil, thedecomposition results are carbon dioxide, hydrogen, methane,ammonia, amines, mercaptans, and hydrogen sulfide(Ponnamperuma 1972).

In an aerated soil, oxygen acts as an electron acceptor, butwhen there is limited oxygen available, as with waterlogging,other compounds become electron acceptors; hence, redoxpotential is reduced. A lowered redox potential affecting O2,NO3

–, Mn4+, Fe3+, SO42–, CO2, and H+ can result in potentially

toxic chemicals accumulating within the rhizosphere (Ohlsson1979).

In waterlogged soils, oxygen deficiency predisposes todenitrification, with the consequent rapid loss of nitrate. Sincenitrate is essential for physiological function, plant growth andappearance are quickly affected. Nutrient availability is alsoaffected by soil pH, as it is an important factor controllingchemical reactions within the soil matrix. Under low oxygen,the pH of acid soil changes towards neutral, whereas for alkalinesoils the pH will decrease (Ponnamperuma 1972). Hence, inwaterlogged anoxic soil, the range and availability of nutrientsavailable to plant roots will also change (Neumann and Romheld2012). Plant shoot concentrations of Na, B, Mn, Fe and Al canincrease and may become toxic (Setter et al. 2009), whileconcentrations of NO3

– and SO42– may become deficient.

Plant responses to waterlogging

Different plant species exhibit different degrees of damage dueto a waterlogging event. Sensitivity of plants to waterloggingmight be expected to vary. During a waterlogging event, theoxygen concentration in stagnant water will decrease throughhypoxic (low soil oxygen concentration) conditions to anoxic(zero soil oxygen concentration) conditions. Experiments havebeen done on a variety of species to determine the ‘critical oxygen

Tab

le1.

Overviewof

parametersmeasuredat

location

sarou

ndAustralia

forspecificfieldstud

iesrelatedto

waterlogging

EL,Economicloss;Slope,slope

definitio

n;SY,seedyield;BN,bollnum

ber;DM,drymatter;HI,harvestind

ex;L

Y,lintyield;G

/E,no.ofgrains

perear;D

es.,description;Top.,topo

graphy;M

C,m

oisturecontent;

AFP,air-filledporosity;W

A,w

aterapplied;RF,rainfall;ET,evapotranspiration;T,tem

perature;L

WP,leafw

aterpotential;LG,leafg

rowth;P

N,plantnutrientlevels;O

D,observedplantdegradatio

n.Shaded

boxesindicatedataprov

ided

infieldstud

y,em

ptybo

xesindicateno

dataprovided

Crop

Location

Yield

reduction

EL

Soildescription

Clim

ate

inform

ation

Plant

observations

Slope

Field

study/reference

SY

BN

tha–

1DM

HILY

G/E

%Des.Top

.MC

AFP

WA

RF

ET

TLWP

LG

PN

OD

Cotton

Griffith,N

SW

Reicoskyetal.(19

85)

Barley,

wheat

Berriquin,N

SW

Grieveetal.(19

86)

Wheat

Ham

ilton,V

ic.

McD

onaldandGardn

er(198

7)Wheat,lup

ins,oats

Upp

erGreatSouthern,

WA

McFarlane

etal.(19

89)

Wheat,lucerne,triticale

Ham

ilton,V

ic.

MacEwan

etal.(19

92)

Lupin

Beverley,

WA

Daviesetal.(20

00)

Oats,pasture

Kyeam

baValley,

NSW

Jerrem

sandHill

(199

9)Peas

Low

erNorth

ofS.A

ust.

McD

onald(199

5)Cotton

Narrabri,NSW

Bange

etal.(20

04)

Cotton

Narrabri,NSW

Milroy

andBange

(201

3)Pea,fababean,

lentil,

lupin

Sou

thernWA

Siddiqu

eetal.(19

93)

552 Crop & Pasture Science R. E. Shaw et al.

pressure’, the minimum oxygen concentration required beforethe plant starts to shows signs of stress (Girton 1979; Saglio et al.1984), and is generally ~10% at 258C (Meyer et al. 1985; Drew1997). Consequently, many plant species undergo adaptationshortly after waterlogging occurs. Aerobic respiration is affectedthrough a change in the glycolysis process in waterlogged plants(Zabalza et al. 2009). Less ATP is produced, resulting infermentation producing only one-eighteenth of the energy(Bramley et al. 2007), implying that adaptation has a cost thatwill most likely lead to reduced growth and yield.

It has been suggested that the magnitude of the effect ofwaterlogging on yield is related to the occurrence at particulardevelopment stages. Setter and Waters (2003) provide athorough review of plant waterlogging tolerances at differentgrowth stages. In their study, wheat and barley were found to bemore tolerant to waterlogging during tillering and grain fillingthan during pre-emergence, seedling growth, and reproductivestages. However, Setter and Waters (2003) note that there arelimited published studies in the area. Observations at Hamilton,south-western Victoria, indicate that waterlogged soils in thearea result from rainfall exceeding evapotranspiration duringwinter and spring, combined with slow-draining duplex soils.Yields of wheat in the area were ~2 t ha–1 and crops showed signsof uneven, stunted growth and pale leaves. It was estimated thatactual yield was reduced by 70% from a potential regional yield,the reduction being attributed to waterlogged conditions duringanthesis (McDonald andGardner 1987). Subsequent experimentsover 2 years with wheat varieties of different seasonal durationand variable sowing dates were only partially successful inshowing increased yields, although the mechanisms were not

discussed. Given the tolerance and the variability of wheatresponse to waterlogging events (Belford 1981; Meyer andBarrs 1988) this lack of clear response is not unexpected.Previous research has shown that waterlogging reduces tillergrowth of wheat (Cannell et al. 1984; Malik et al. 2002).Condon and Giunta (2003) tested wheat varieties withdiffering tillering potential to see if varieties with ‘restrictedtillering’ were less likely to have reduced yield due to transientwaterlogging. Their results provided little support for theirhypothesis.

Plants that are subject to waterlogging often developsymptoms of leaf yellowing, wilting, root rotting, and rootblackening. As discussed in the previous section, waterloggingcan cause a change in microbial activity within the rhizosphere(Ponnamperuma 1972), which can lead to root rotting andblackening. Curiously, leaf wilting, usually strongly associatedwith water deficit, has been observed in several species underwaterlogged conditions. Tomato leaves wilt when waterloggedfor 4–6 h (Jackson 2002). Tobacco plants undergo leaf yellowingand wilting when waterlogged for 2 h (Kramer and Jackson1954). The aboveground symptoms are often the firstindicators that crop yield may be reduced. Almost certainly,the aboveground symptoms are indicating that conditionsbelow the ground are suboptimal.

Despite symptoms of stress becoming apparent, plants mayalso show adaptation responses. Below-ground adaptations caninclude adventitious root, aerenchyma, and radial oxygen-lossbarrier development and a change in root hydraulic conductance.Adventitious roots can appear at, or slightly above, a saturatedsoil, or a free water surface in inundated situations. These roots

Table 2. Summary of observed effects of waterlogging on crop and pasture yields and reported estimates of economic loss for locations aroundAustralia

Blank cells represent unavailable data

Crop Location Irrigated Yieldreduction

Survey area(ha)

Economic loss(AU$ year–1)

Reference

Barley Berriquin, NSW Y 20% 333 300 142 000 Grieve et al. (1986)Wakool, NSW Y 20% 34 000 12 000 Grieve et al. (1986)

Wheat Berriquin, NSW Y 20% 333 300 1.4million Grieve et al. (1986)Wakool, NSW Y 20% 34 000 125 000 Grieve et al. (1986)Hamilton, Vic. – 70% – – McDonald and Gardner (1987)Upper Great Southern, WA – 14% – 11million McFarlane et al. (1989)Hamilton, Vic. Y 65–78% – – MacEwan et al. (1992)

Lupins Upper Great Southern, WA – 8% – 0.5million McFarlane et al. (1989)Yellow lupins Beverley, WA N 10% Experimental field plot – Davies et al. (2000)Narrow leafed lupins Beverley, WA N 61% Experimental field plot – Davies et al. (2000)Oats Upper Great Southern, WA – 12% – 1.6million McFarlane et al. (1989)

Kyeamba Valley, NSW Y 24% 150 500 1.2million Jerrems and Hill (1999)Lucerne Hamilton, Vic. Y 57% – MacEwan et al. (1992)Annual grazing

pastureKyeamba Valley, NSW Y 21% 150 000 – Jerrems and Hill (1999)

Perennial grazingpasture

Kyeamba Valley, NSW Y 24% 150 000 – Jerrems and Hill (1999)

Peas Lower North of South Australia – 2–45% Experimental field plot – McDonald (1995)Cotton Narrabri, NSW Y 37% Experimental field plot – Bange et al. (2004)

Griffith, NSW Y 50% Experimental field plot – Reicosky et al. (1985)Pea Lake Grace, WA – 70% Experimental field plot – Siddique et al. (1993)Lentil Lake Grace, WA – 23% Experimental field plot Siddique et al. (1993)Albus lupin Esperance, WA – 55% Experimental field plot – Siddique et al. (1993)

Waterlogging in Australian landscapes Crop & Pasture Science 553

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554 Crop & Pasture Science R. E. Shaw et al.

have greater access to atmospheric oxygen and generally remainviable until the upper soil layers dry. Barley (Garthwaite et al.2003) and wheat (Belford 1981) develop both adventitiousroots and aerenchyma during waterlogging. Aerenchyma canbe formed through two processes. Lysigenous aerenchymaforms in situ through the death of cortex or pith cells withinroots (Colmer 2003), or by new specialist cell growth withinroots called schizogenous aerenchyma. Aerenchyma increasethe gaseous root porosity by 15–50% of the total root volume(Colmer 2003), and hence significantly decrease the resistanceto diffusive oxygen flux from the top of the plant to the root, andpotentially into the rhizosphere. This radial diffusion of oxygenfrom the aerenchyma, through the epidermal root cell layers, andinto the surrounding rhizosphere across the oxygen concentrationgradient is described as the radial oxygen loss (ROL). Armstrong(1980) estimated that 30–40% of the oxygen in the aerenchymacan be lost through this radial diffusion pathway. Some plants(e.g. rice and some barley species; Colmer 2003) form a ROLbarrier (ROLB) in the root basal zone, which decreases theoxygen loss, hence increasing the effectiveness of oxygenmovement longitudinally along the concentration gradienttowards the root apex. The ROLB of rice is formed by a layerof sclerenchymatous fibre with thick secondary walls on theexternal sides of the cortex (Colmer et al. 1998). Theformation of a ROLB seems to be species-specific. Forexample, cultivated barley (Hordeum vulgare L.), whiledeveloping aerenchyma and adventitious roots in low oxygenavailability conditions, does not develop a ROLB (Garthwaiteet al. 2003). In the same genus, wild barley (Hordeum marinumL.) develops adventitious roots, aerenchyma, and a ROLB insimilar conditions.

The ease with which water can move into and through theroots, root hydraulic conductivity, has been shown to change inlow oxygen or waterlogged conditions (reviewed by Bramleyet al. 2007). Root hydraulic conductivity includes the radialflow path from the root surface into the xylem vessels and thelongitudinal or axial flow path through the xylem vessels to thecrown of the plant. The flow J (m3 s–1) is defined as:

J ¼ Dy:L

whereDy is the potential gradient (kPa), andL is the conductivity(m3 s–1 kPa–1).

It should be noted that as in the physical electrical analogue,conductance (K) and resistance (R), and conductivity (L) andresistivity (r), are inversely related and used interchangeably as:

K ¼ 1=R

L ¼ 1=r

R ¼ r:length=area

Theuseof the electrical analogue as the conceptual frameworkto describe water movement in plants promulgated theassumption that L was largely constant (see Meyer 1976), eventhough experimental evidence indicated that this may not betrue (Greacen et al. 1976). The assumption has continued to beused in the implementation of root hydraulic conductance into

crop growth and yield models. With the discovery of a specificprotein, aquaporin (Agre et al. 1993), it was demonstrated thatthe permeability of animal cell membranes (and later plant cellmembranes; Maurel 1997) to water could change by inserting orwithdrawing aquaporins from themembrane. Upon insertion, theaquaporin acts as a channel across the membrane through whichwater can move passively down a potential gradient, henceincreasing water permeability. In plants the situation appearsmore complex, since it has been shown that aquaporins aremore diverse, there being many more genes encoding plantaquaporins, and that the individual protein pores can be openand shut (gated) by a variety of signals (Tyerman et al. 1999). Thisincludes rapid closure under anoxia which is signalled by areduction in the pH of the cell cytoplasm (Tournaire-Rouxet al. 2003). The observation that the presence of mercuricchloride inhibited aquaporin activity, resulting in reducedhydraulic conductivity, supports the causative mechanism(Bramley et al. 2010). Other blockers can be used to show thesame effect, including root acidification with a weak acid, whichreplicates the signal that occurs under anoxia. It follows thenthat any exogenous treatment that inhibits or enhances theexchange of aquaporin with the root cell membranes couldhave a significant effect on L. Variation in L can be as muchas 10-fold in some species and there is generally a linear andpositive correlation with the transpiration rate (Vandeleur et al.2013). Thus:

L ¼ b� E þ C

where E is whole plant transpiration rate, and b and C areparameters determined by the linear regression of theexperimental relationship found by Vandeleur et al. (2013). Insome species (e.g. grapevine), C is close to 0; i.e. at lowtranspiration rates the root L can approach 0. For soybean, C issignificant and positive so that at low E there is still significantroot L. This function connectingE and L needs to be incorporatedinto models to consider a more complete effect of soil andatmospheric conditions on plant performance. Further researchis required comparing physiological effects and hydraulicconductivity reductions under waterlogged conditions, sincepublished results show large differences in L between plantvarieties and the duration of waterlogging (Table 3).

The role that aquaporin activity may play in determining theresponse of plants to waterloggingwarrants further investigation,as stated by Parent et al. (2008) in a recent internationalreview. For example, the observation that some species exhibitwilted leaves when their root systems are oxygen-deprived(Table 4) suggests that L has decreased substantially. Itindicates that for the function above there has been adisruption in the normal signalling that matches shoottranspiration to the capacity of the root to transport water. Theappearance of wilting in waterlogged plants has generally beenascribed to effects of ethylene production by roots rather than toan insufficiency of water to maintain leaf turgor. It has alsobeen shown that ethylene can have an effect on the activity ofaquaporins (Kamaluddin and Zwiazek 2002; Chervin et al. 2008;Tungngoen et al. 2009). However, the observation of alteredaquaporin activity, lower hydraulic conductance, and leafwilting with hypoxic conditions suggests that the leaves of

Waterlogging in Australian landscapes Crop & Pasture Science 555

waterlogged plants are water deficit stressed. This hypothesisneeds testing.

From the discussion above it is evident that plants undergomany physiological and structural adaptations in low oxygenconditions. Representing these adaptations both conceptuallyand as simulation models is very challenging but ultimatelydesirable to provide more reliable estimation of responses.Thus far, the potential role of aquaporin in modifying rootwater uptake has not been included in any plant growth andwater use model. Logically, it should be and the effectivenessand anticipated improvement in model performance should betested.

One hypothesis is that a major effect of waterlogging andtherefore reduced root-zone oxygen supply is reduced watersupply to the leaf canopy of the plant, which in turn affectsplant growth and yield. If this hypothesis can be supported thena different representation of this process needs to be includedin simulation models and its effectiveness tested against fieldobservations.

Using models to simulate plant responses to waterlogging

There are several models used for predicting crop growth andyield, such as CropSyst (Stockle et al. 2013), CROPGRO (Booteet al. 1998), Decision Support System for AgrotechnologyTransfer (DSSAT) (Jones et al. 1998), Crop Estimationthrough Resource and Environment Synthesis (CERES)(Ritchie et al. 1998), System Approach to Land Use andSustainability (SALUS) (Basso et al. 2006), SWAGMAN®

Destiny (Meyer et al. 1996), DRAINMOD (Skaggs 2008), andthe Agricultural Production Systems Simulator (APSIM)(Asseng et al. 1998). However, only three of the listed modelspredict yield reduction from waterlogging, SWAGMANDestiny, DRAINMOD, and APSIM (Table 5), and these willbe further discussed here.

SWAGMAN (Salt Water And Groundwater MANagement)Destiny is a management tool developed by CSIRO Land andWater designed to estimate growth and yield of a range of cropsthat are affected by salinisation, shallow groundwater, waterdeficit, and waterlogging. SWAGMAN Destiny inputs weatherdata as well as soil, crop, and agronomic information thatcharacterises the crop system of interest. It assigns thepotential yield that could be expected for the crop of interest,and then calculates the likely effects on crop growth of dailyedaphic and climatic stresses. The accumulation of the variousabiotic stresses (water deficit, aeration, nitrogen insufficiency,and salinity) is used to reduce the potential yield and henceestimate an actual yield. SWAGMAN Destiny is primarily a

Table 3. Reduction in hydraulic conductivity (L) for several plantspecies after varying periods of waterlogging

Plant Timewaterlogged

Reductionin L

Reference

Mouse-ear cress 30min 42% Tournaire-Roux et al. (2003)Maize 2 h 27% Gibbs et al. (1998)Bananas 4 h 43% Aguilar et al. (2003)Sunflower 16 h 50% Everard and Drew (1989)Tomato 24 h No change Jackson et al. (1996)

Table 4. Leaf wilting observed for several plant species after varyingperiods of waterlogging

Plant Time to wilting afterwaterlogging

Reference

Tomato <3 h Jackson (1956)Castor oil 3 h Else et al. (2001)Tobacco <24 h Kramer and Jackson (1954)Sunflower <24 h Grassini et al. (2007)Geraniums 4–5 days Bergman (1920)Bean 4–5 days Bergman (1920)

Table 5. Inputs, outputs, and stresses considered for three crop growth and yield models used to simulate stress effects including waterlogging

SWAGMAN Destiny DRAINMOD(including sub-modules)

APSIM(including sub-modules)

Inputs Weather data � � �Soil profile properties � � �Crop parameters � � �Agronomic information � �Watertable and groundwater specification �Irrigation data � �Management data �Drainage design �

Outputs Crop duration, dry matter and yield � � �Water balance components � �Watertable depth � �Soil water distribution � � �Drainage amounts � � �Soil salinity profile �

Stresses represented Water deficit � � �Aeration � � �Nitrogen deficiency � � �Salinity � � �Delayed planting date �

556 Crop & Pasture Science R. E. Shaw et al.

generic plant growth, water use and yield model. Plant dry matteraccumulation is driven by intercepted radiant energy, whereasplant phasic development is dependent on ambient temperature.Soil water availability is estimated in a layered soil profile withinspecified upper and lower volumetric water content limits.Redistribution of water in the profile uses a modified ‘tippingbucket’ representation, while upflow (capillary rise) fromsaturated lower layers is calculated from a normaliseddiffusivity value and the volumetric water content. Input dailyweather data are used to calculate atmospheric evaporativedemand. Actual plant water use is controlled by the leaf areaindex and expression of any calculated stress caused byinsufficient water availability from the soil root-zone. Themodel runs on a daily time-step and keeps track of potentialgrowth in the absence of any growth-reducing stress. Stresseffects are implemented as normalised zero to unity valuesassociated with water deficit, aeration, nitrogen availability,and salinity. When multiple stresses occur on any day, themost severe of these stresses is used to scale back potentialgrowth to output estimated actual growth values. Final yield,either as dry matter or grain yield (as species appropriate), iseffectively an input potential yield scaled back by the combinedeffects of various stresses accumulated over the growing season.

The current version of SWAGMAN Destiny representswaterlogging by calculating the gas-filled soil pore volume asa function of soil water content and identifying a critical volumeat which point the availability of oxygen is deemed insufficient.A set lag time of 3 days occurs, and if the pores are still filledwith water, an aeration stress index is calculated. If the aerationstress is the most limiting stress then dry matter accumulation bytheplant is reduced, andhenceyield is reduced.Previousworkhasbeen undertaken on SWAGMANDestiny and validated throughcalibration of field data inputs. Throughout the duration of amaize silage crop a close match between estimated andmeasureddata was found for watertable depths and volumetric soil watercontent at different depths (Edraki et al. 2003). However, it wasalso indicated that a more comprehensive investigation of soiltype and salinity, along with different crops and watertablelevels, would be advantageous and therefore the modelrequired further improvement. Observations and SWAGMANDestiny results were compared for three varieties of wheat inboth lysimeter and field conditions in south-eastern Australia(Godwin et al. 2002). A range of soil and crop parameters wasused for a range of site and management conditions, andexcellent agreement between estimated and observed yieldvalues, with good comparisons of soil volumetric watercontent, root length density, evapotranspiration, and leaf areaindex, was found.

DRAINMODsimulates the effectiveness of drainagebasedonthewater balance in the soil profile. DRAINMODwas developedby Dr Wayne Skaggs at the North Carolina State University in1980. It estimates watertable depths, soil water conditions, anddrainage rates by input of soil properties, crop parameters,drainage design parameters, weather parameters, and irrigationdata. It calculates infiltration, evapotranspiration, drainage,surface runoff, sub-irrigation, deep seepage, lateral seepage,watertable depth, and soil water status (Skaggs 2008). Cropyields have been analysed using DRAINMOD, and modelvariations have been written to include crop parameters

(Hardjoamidjojo et al. 1982; Wang et al. 2006; Hassanpouret al. 2011; Kale 2011). As with SWAGMAN, DRAINMODcalculates the relative yield using the assigned potential yieldand the reduced crop yield due to combined stresses. The relativeyield depends on factors specific to individual crops and thewatertable. Crop factors are determined experimentally (Evanset al. 1990). DRAINMOD estimated drain flow, along with cornand soybean yields, at different drainage spacing over a 15-yearperiod for environmental conditions similar to those experiencedin the area between south-western Ohio and southern Illinois(Wang et al. 2006). Input data included weather data, drainagesystem parameters, soil properties, crop parameters, and soiltrafficability parameters. The maximum effective rooting depthand effective rooting depth–time distribution were cropproperties used. Output yields for both corn and soybean wereoverestimated by DRAINMOD when comparing the observedand estimated values. DRAINMOD was used to predict thesurface drainage and yield of canola in waterlogged fields ofIran during cold conditions (Hassanpour et al. 2011). Climatedata, drainage system parameters, and information on soil andplants were inputs. Relative yield was calculated using the StressDay Indexmethod (Hiler 1969). The results again showed anoverestimation of yield compared with observed yield. Additionalmodel research was recommended to improve estimationaccuracy.

APSIM was developed by the Agricultural ProductionSystems Research Unit, a collaborative group of CSIRO andQueensland State Government agencies. APSIM uses acollection of various modules—such as crop growth, soilwater (SOILWAT), surface residue (RESIDUE), soil organicmatter and soil nitrogen supply (SOILN), and soil pH(SOILpH)—to take account of interactions between soil water,nitrogen, and plant residues on crop growth, development, andyield. Weather, soil, crop, and management data are inputs.

The module SOILWAT estimates soil water balance andhas evolved from modifications to the models CERES andPERFECT (Productivity Erosion Runoff Functions to EvaluateConservation Techniques) (Littleboy et al. 1989). The watercharacteristics of lower limit, drained upper limit, saturated,and volumetric water contents and soil layer thickness arespecified. Processes of runoff, evaporation, saturated flow,unsaturated flow, and movement of solutes are represented(Keating et al. 2003). SOILWAT, like SWAGMAN Destiny,uses a ‘tipping bucket’ approach to redistribute water betweenlayers, while a perched watertable is calculated as the proportionof water content between the drained upper limit and saturationin a layer when the immediate layer below is saturated (Assenget al. 1997).

The module NWHEAT is an APSIM-Wheat module used todetermine wheat crop growth and yield. NWHEAT includesthe effects of aeration stress on the root system of wheat(Asseng et al. 1998). Aeration deficit effects are based onassumptions from experiments performed in the 1980s and1990s (experimental data sources listed in Asseng et al.(1998). After 3 days of aeration deficit in the root-zone, anaeration stress is calculated on a zero to unity scale. Athreshold of filled water within a layer is set, and below thatthreshold, root depth is affected and the root length density is setto zero within that saturated layer. The sensitivity to aeration

Waterlogging in Australian landscapes Crop & Pasture Science 557

stress is crop-dependent and based upon the crop growth stage,with crop emergence at maximum sensitivity (zero) and grainfilling at no sensitivity (unity). Implementation of the aerationfactor results in a reduction in root depth by removing all rootsfrom 5 cm below the upper edge of the affected part of a rootsystem in a saturated soil layer (Asseng et al. 1998). Thresholdsbased on the root aeration factor determine the leaf area growthand tillering of the wheat crop and consequently predict yield.

Bassu et al. (2009) used APSIM-Wheat to simulate the effectof waterlogging on durum wheat at various sowing dates andcompared the yield estimates with experimental field work(over 2 years) and past literature (14 years of data from theExperimental Institute for Cereal Crops). To improve theestimates, APSIM-Wheat was modified with a relationshipbetween sowing date (hence ambient temperature) and the

time between leaf appearances (the phyllochron interval) fordifferent cultivars. The simulation results showed that earlysowing in frequently waterlogged areas had no benefit;however, early sowing in areas with low to moderatewaterlogging risk could increase crop yield.

Ageneric cropmodel has beendesigned forAPSIM(GCROP)that takes into account plant physiological processes commonacross crop species (Wang et al. 2002). They noted thatphotosynthetic rate, crop-specific radiation use efficiency, leafarea expansion, root growth and distribution in soil, crop uptakeof water and nitrogen, and potential crop transpiration are allprocesses considered in current crop models and attempted tocombine them into one generic crop model. The uptake of waterby the crop is determined by the crop demand and the availablesupply (potential uptake) from the soil.Whilst GCROP combines

Table 6. Overview of the soil process routines for three crop growth and yield models used to simulate stress effects including waterlogging

SWAGMAN Destiny DRAINMOD (including sub-modules) APSIM (including sub-modules)

Soil water availability Estimated by layers. Volumetric watercontent defined using saturated,drained upper limit and lower limit

Estimated by layers. Soil water statuscalculated using a Richards equationsolution. Inputs needed include soilwater characteristic relations andhydraulic conductivity estimates.Waterbalance calculated on a basic 1 h time-step, but when there is minimummovement of watertable position time-steps are changed to 1 day and whenrainfall exceeds infiltration time stepsare �0.1 h (Skaggs 1991)

Estimated by layers. Volumetric water contentdefined using saturated, drained upper limitand lower limit

Redistribution of water Inferred mass flow using a tippingbucket representation

Uses the drained-to-equilibriumassumption with a two-dimensionalRichards equation for saturated andunsaturated flow (Skaggs 1991)

Inferred mass flow using a tipping bucketrepresentation

Upflow from saturatedlower layers

Calculated from a normaliseddiffusivity value and volumetricwater content

Maximum water movement andwatertable depth are defined for eachsoil by numerically solving an equationrelating unsaturated soil hydraulicconductivity and the pressure head forvertical unsaturated movement due toET on the surface (Skaggs 1978)

Unsaturated water flow is calculated bydiffusivity as a function of volumetric watercontent (Probert et al. 1998)

Process used to inferwaterlogging

Gas filled soil pore volume is afunction of soil water content. Acritical volume is definedwhenO2 isinsufficient for root growth andfunction. A set lag time of 3 daysoccurs and if the soil pores are stillfilled with water a 0–1 aerationfactor is assigned reducing thepotential yield

Predicted yield due to excessive soil wateris dependent on the stress day index(SDI). The SDI is calculated if thewatertable depth is within the root-zone(taken to be the top 30 cm(Hardjoamidjojo et al. 1982; Skaggs1991). The SDI is calculated using cropfactors (determinedexperimentally) andthewatertabledepth (Skaggs1991).SDI<141 relative yield calculated; SDI�141 relative yield is 0 (Skaggs 1991)

Aeration is considered after 3 days for eachlayer containing roots and a 0–1 aerationstress is calculated, based on the saturatedsoil water content, the soil water content,and the drained upper limit. A threshold offilled water within a layer is set and belowthat threshold root depth is affected

Effect of waterloggingon growth and yield

Drymatter accumulation of the plant isreduced, hence reducing thepotential yield. The 0–1 stress (inthis case aeration) factor iscalculated and thenused to influenceroot growth, death, and hencedistribution. It also affects canopygrowth

Effect of waterlogging on yield iscombined with effects of drought anddelayed planting date to reduce thepotential crop yield (Skaggs 1991).Effect of waterlogging on roots is user-controlled with the opportunity to enterknown rooting depths duringwaterlogging conditions. (Cox et al.1994)

Root depth and leaf area growth are reducedand hence predicted crop yield is reduced.Implementationof the aeration factor resultsin a reduction in root depth by removing allroots from 5 cm below the upper edge of theaffected part of a root system in a saturatedsoil layer (Asseng et al. 1998). Stress factorvaries depending on plant stage, frommaximum (0) at emergence to no sensitivity(1) at grainfill (Asseng et al. 1997)

558 Crop & Pasture Science R. E. Shaw et al.

the current plant physiological processes being modelled, it stilldoes not consider detailed physiological adaptations due tospecific environmental stresses such as waterlogging.

Using models with representations of the major physiologicalprocesses and the environmental drivers (radiant energy,temperature, water, nutrients), it is possible to diagnose thecontributing effect of various stresses in a retrospective wayand, importantly, to identify areas and conditions that willlikely lead to yield reduction. With this understanding,improved management for optimised yield outcomes should bepossible, giving farmers the tools to make informed decisionsconcerning cropping in waterlogged areas. However, the successof these models is highly dependent on the adequacy of theprocess representations (compared in Table 6) in the simulationmodel. As better understanding of plant physiological processesdevelops, process representations within the models should bemodified. None of the three models discussed above includes thephysiological adaptations that root systems undergo duringwaterlogging. One of the changes identified most recently is achange in total root system hydraulic conductance (Bramley andTyerman 2010). This change in conductance is the result ofchanges in aquaporin activity that influence water transportacross root cell membranes (Bramley et al. 2010). Inclusion ofthe change in hydraulic conductance of root systems duringwaterlogging as an extra module adaptable to all the cropgrowth and yield models within the simulations may improvemodelling predictability, increasing the accuracy of predictedyield outcomes. An improved diagnostic and managementoption assessment tool would then be available.

Conclusion

It is evident that plants have a range of adaptive features torespond to waterlogging, although they often exhibit reducedgrowth and, ultimately, reduced crop yield. Despiteinconsistencies between studies in the parameters used toassess effects of waterlogging, there is sufficient evidence thatwaterlogging reduces crop growth and yield. Surface and subsoildrainage has been successful in reducing yield loss fromwaterlogging stress. Evidence also shows that it is important tokeep watertables below a critical depth to minimise soil salinity.Effective soil drainage techniques are known to alleviate thegrowth and yield reduction effects of both waterlogging and soilsalinity.

There is enormous scope for improvement in the predictionand measurement of waterlogging in the field. Currently, noconsistent measurements are being recorded to facilitatedecisions regarding drainage and/or crop selections based onthe potential for waterlogging in any defined area; therefore, aminimum dataset should be established. Additionally, muchgreater understanding of the physiological mechanisms behindthe reduction in crop yield is required, such as the relationshipsbetween changes to aquaporin activity, reduced root hydraulicconductance, and wilting.

Combining this information should enable a better predictionof potential waterlogged areas and improve estimates of thelikely extent of reduced crop growth resulting in reduced yielddue to waterlogging. Modifying current models to include aminimum dataset along with updating process representations

to encapsulate the understandings of plant physiologicaladaptations will improve models as diagnostic andmanagement tools. An improved model will greatly enhancethe information available to farmers when they need to makedecisions based on the multitude of effects caused bywaterlogging. Improved estimates of the effects of transientwaterlogging will give growers and advisers a clearerindication of the merit of attempting to decrease effects ofwaterlogging through species and variety selection, surfacedrainage, or subsurface drainage. Combining the bio-physicalprojections with financial information could also indicate whensuch changes could be financially non-viable. Such well-informed decision-making could save industry thousands ofdollars in wasted efforts.

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