Natural Resources, 2010, 1, 34-56
doi:10.4236/nr.2010.11005 Published Online September 2010 (http://www.SciRP.org/journal/nr)
Modelling Irrigation and Salinity Management
Strategies in the Ord Irrigation Area
Riasat Ali1, John Byrne1, Tara Slaven2
1CSIRO Land and Water, Private Bag 5, Wembley, Australia; 2Department of Agriculture and Food, Western Australia, Kununurra,
Australia.
Received September 6th, 2010; revised September 27th 2010; accepted September 30th, 2010.
ABSTRACT
The Ord River Irrigation Area (ORIA) is located within no rth ern Western Australia near the Northern Territo ry bord er.
Since the beginning of irrigated agriculture in the ORIA the groundwater levels have been continuously rising and are
now close to the soil surface in some parts of ORIA in northern Western Australia. The groundwater is now saline
throughout most of the ORIA and soil salinity risks are high where the watertables are shallow. This research evaluated
irrigation and sa linity managemen t strategies for sugarcane and m aize crops grown o ver deep and shallo w, non-saline
and saline watertables in the ORIA. The LEACHC model, calibrated using field data, was used to predict the impa cts of
various irrigation management strategies on water use and salt accumulation in the root zone. This study concluded
that irrigation application equal to 100% of total fortnightly pan evaporation applied at 14 day intervals was a good
irrigation strategy for the maize grown over a deep watertable area. This strategy would require around 11 ML/ha of
irrigation water per growing season. Irrigation application equal to 75% of total fortnightly pan evaporation, applied
every fortnight during first half of the growing season, and 75% of total weekly pan evaporation, applied on a weekly
basis during second half of the growing season, would be the best irrigation strategy if it is feasible to chang e the irri-
gation interval from 14 to seven days. This irrigation strategy is predicted to have minimal salinity risks and save
around 40% irrigation water. The best irrigation strategy for sugarcane grown on Cununurra clay over a deep watert-
able area would be irrigation application equal to 50% of the total fortnightly pan evaporation, applied every fortnight
during first quarter of the growing season, and irrigation application amounts equal to 100% of total weekly pan
evaporation, applied every week during rest of the season. The model predicted no soil salinity risks from this irrigation
strategy. The best irrigation strategy for sugarcane over a non-saline, shallow watertable of one or two m depth would
be irrigation application amounts equal to 50% of total fortnightly pan evaporation applied every fortnight. In the case
of a saline watertable th e same irrigation strateg y was predicted to the best with resp ect to water use efficiency bu t will
have high salinity risks without any drainage management.
Keywords: Irrigation Modelling, Salinity Modelling, Saline Shallow Watertable, Irrigation Man a gement, Ord River
Irrigation Area
1. Introduction
Hydrological conditions change with the introduction of
irrigated agriculture in almost any landscape. Increased
accession to groundwater starts at the commencement of
irrigated agriculture and over time it brings groundwater
levels closer to the soil surface and leads to the develop-
ment of shallow watertables. Evapotranspiration from
increased availability of water from shallow watertables
is the main cause of soil salinisation in irrigated areas
throughout the world [1] and Australia [2]. Availability
of abundant water, low population pressures and lack of
awareness of the long term implications of excessive use
of water has led to the problems of waterlogging and
irrigated salinity in a vast majority of the old irrigation
systems of the world. Today because of changing climate,
high population pressures, water scarcity and increased
awareness of the long term implications of excessive use
of water every effort should be made to use this resource
optimally to enable more production from less water thus
reducing wastage via groundwater accession and runoff.
In the Ord River Irrigation Area (ORIA) in northern
Australia (Figure 1) the groundwater levels were deeper

Modelling Irrigation and Salinity Management Strategies in the Ord Irrigation Area35
Figure 1. Map showing the location of the study area in northern Western Australia.
before clearing the native vegetation for irrigated agri-
culture. With the introduction of irrigated agriculture, the
groundwater levels started rising with increased deep
drainage below irrigated fields due to excessive use of
irrigation water, and leakage from unlined supply chan-
nels and drains servicing the area. The groundwater lev-
els continued to rise at 0.3 to 0.5 metres per year beneath
most of the central and northern parts of Ivanhoe Plain
over time [4]. Until 1990s the groundwater levels were
sufficiently deep to prevent any significant capillary
aided evapotranspiration and soil salinsation risks [3].
They are now relatively close to the soil surface in some
parts of the Ivanhoe and packsaddle plains (Figure 2).
Due to the changed hydrological conditions, the chemis-
try of groundwater probably changed over time [5]. The
shallow groundwater electrical conductivity (EC) varies
throughout most of the ORIA with levels ranging from
50 to 2160 mS/m [6]. In some parts of the Ivanhoe and
Packsaddle plains, the shallow groundwater salinity (EC)
is at extreme levels. Because the groundwater is shallow
and saline in the ORIA, the risk of developing soil root
zone salinity is high. Saline watertables shallower than
two metre below ground surface often lead to the devel-
opment of soil root zone salinity [7] and [8].
This study was aimed at evaluating water and salinity
management strategies for maize and sugarcane crops
grown on Cununurra clay in the ORIA. The impacts of
both fresh and saline shallow watertables on the water
demands and soil root zone salinity were evaluated
through modelling. The objectives were to:
2. Ord River Irrigation Area
The ORIA is located at Kununurra in the East Kimberley
region of Western Australia near the Northern Territory
border (Figure 1). It occupies around 16,000 ha along
the palaeo-alluvial flood plain of the lower Ord River.
The land surface in this irrigation area varies by only
about 10 m with the surrounding sandstone and basalt
ranges outcropping up to around 400 m above the allu-
vial plain. Presently Stage 1 of the ORIA consists of
around 12,000 ha of irrigated agriculture serviced by
approximately 135 km of clay-lined supply channels and

Modelling Irrigation and Salinity Management Strategies in the Ord Irrigation Area 36
Figure 2. Mean observed watertable depth beneath Ivanhoe and Packsaddle plains between July 2003 and June 2004 [4].
155 km of surface drains. The return flow from flood and
furrow irrigation systems discharges back into the lower
Ord River.
2.1. Climate
The climate of this region is semi-arid with summer
monsoonal rains. Around 90% of the annual rain is re-
ceived between November and March. Average wet-season
rainfall (July-June) is around 800 mm but is highly vari-
able. Pan evaporation is around 3000 mm per year [9].
Mean monthly pan evaporation exceeds rainfall through-
hout the year except February. The mean minimum and
maximum temperatures are around 14°C and 30°C in
July and 25°C and 39°C in November.
2.2. Ord Soils
The dominant soil types include cracking clays from the
Cununurra and Aquitaine families. Levee type soil and

Modelling Irrigation and Salinity Management Strategies in the Ord Irrigation Area37
sands also exist. The Cununurra clays occur in normal,
alkaline and leached phases. Detailed information relat-
ing to soils in the Ord River area can be found in [10-12];
and [13]. The normal phase of Cununurra Clay occurs in
large areas of the Packsaddle Plain and has a dark colour
with medium texture and poor drainage. The alkaline
phase consists of imperfect to poorly drained brown
clays with fine topsoil and exists in a large area south of
the Kimberly Research Station on Ivanhoe Plain. The
leached phase occurs in the north and east. It generally
has a coarser structure and higher clay content and poor
to very poor drainage. Aquitaine soils are bluish-grey to
yellow cracking clays and exist in areas subjected to
prolonged inundation, such as swamps, and have very
poor drainage. Smaller areas of the alkaline and acid
phases also occur. Packsaddle loamy sands exist adjacent
to the Cununurra clays. These are better drained and well
suited to intensive horticultural activities. The light tex-
tured Ord loamy sands are located near the river. Sand
and gravel beds of the old palaeochannel of the Ord
River underlie more than 60 percent of the ORIA. These
beds form extensive interconnected aquifers under irri-
gated areas of the Packsaddle and Ivanhoe plains.
2.3. Main Crops and Irrigation Methods
To identify irrigable areas in the Ivanhoe and Packsaddle
plains, a detailed survey of the area was conducted in
1944. The irrigation development project was imple-
mented in stages. Initially, only five farms on Ivanhoe
Plain were released for irrigation in 1962. By 1969, a
total of 30 farms (5,540 ha) were released. The irrigated
area increased by 200% to around 11,000 ha between
1990 and 1999. More than half of over 100 active farms
are small and rely on off-farm income. About 40 farms
are large-scale where a variety of crops are grown. The
main crops include sugarcane, maize, chickpea, sun-
flower and horticultural including melons, pumpkin,
mangoes, bananas citrus and sandlewood. Recently irri-
gated sandlewood plantations have increased substan-
tially. Sugarcane introduced during 1990s is one of the
major crops in the ORIA and has more than double the
water requirements of most other crops [14]. Irrigated
crops are generally grown during the dry season when
growing conditions are best [15]. The irrigated fields are
mostly fallow during the wet season except annual crops
such as sugarcane.
The value of the main crops ranges between $60 and
$80 million per annum. The sugarcane, melons and
sandlewood produce the highest values. The average
output values per hectare of cropped area range from
$2,500 to over $17,000. Three high value crops include
bananas ($17,200 per ha), melons ($13,600 per ha) and
pumpkin ($18,800 per ha). The sugarcane has a relatively
low value ($4,200 per ha). Although bananas produce the
highest per hectare values their production has almost
ceased recently. The sandlewood values almost three
times the value of sugarcane; increasing areas of sandle-
wood plantations are indicative of their high returns.
Common irrigation methods are furrow, sprinkler and
drip. Intensive tree crops and bananas, grown on sandier
soils, are irrigated using sprinkler and drip irrigation
methods. The clay soils are better suited for broad acre
farming. On these clay soils, the furrow irrigation is used
for most broad acre crop production including sugarcane
and maize. Fields are laser levelled to a gradient ranging
between 1:800 and 1:2,000. The furrow lengths are often
200 m long and rarely longer than 500 m. The beds are
mostly 1.8 m wide and 0.16 m high above furrow. The
height between the surface water in the water course and
the tumble area of the furrow is called irrigation head and
typically ranges between 100 and 250 mm. The water
supply from the water course to the furrow is through
siphon whose diameter ranges between 25 mm and 50
mm depending on the furrow length and water supply
rate.
Irrigation interval and application amounts vary de-
pending on crop type, growing stage, weather and farmer.
They typically range from one week to more than a
month and are not optimal. Usually irrigation application
amounts are significantly larger than the required amounts
determined based on soil moisture deficit. This results in
excessive deep drainage and groundwater accessions and
runoff from irrigated fields. This also results in applica-
tion of irrigations when either too much moisture is still
available from an earlier irrigation or the crop is under
stress due to insufficient soil moisture.
2.4. Irrigation Water Availability
The water allocation for Stage 1 (about 11,000 ha irri-
gated area) is 350 GL per year. An allocation of 400 GL
per year is set for irrigation of new area of about 14,000
hectares in Stage 2. The irrigation water is supplied by
constructing a Kununurra diversion dam on the Ord
River and M1 supply channel network. The Kununurra
diversion dam, a 20 m high structure that forms Lake
Kununurra of 101 GL storage capacity, holds water in
the Ord River water course for approximately 50 km up-
stream. The Ord River Dam, located approximately 60
km upstream in the Carr Boyd Ranges, was constructed
to store water in Lake Argyle to ensure a reliable supply
of irrigation water to the ORIA. The water is released
from Lake Argyle and stored in Lake Kununurra which
provides the head necessary to divert water to irrigation
areas in the ORIA. Water levels in Lake Argyle therefore
dictate any restriction policies for water demands.
The average annual water availability from Lake Ar-

Modelling Irrigation and Salinity Management Strategies in the Ord Irrigation Area 38
gyle is 4257 GL. The Lake Argyle water is diverted for
irrigation use, hydropower generation and environmental
releases. Currently only about 8 percent of the available
water is used for irrigation in Stage 1 of the ORIA. With
the introduction of Stage 2 irrigation area the level of use
for irrigation is projected to increase to about 17 percent.
If all controlled releases such as irrigation, environmental
and power generation are included the level of use in-
creases to about 57 percent of the available water. Under
a wet future climate increased inflows into Lake Argyle
are expected and the annual water availability is pro-
jected to increase to about 5110 GL and the relative level
of use for controlled releases is projected to reduce to
about 50 percent. Under a dry future climate, due to de-
creased inflows into Lake Argyle, the annual water
availability is expected to reduce to about 3320 GL and
relative level of water use for controlled releases is likely
to increase to about 64 percent of the total available wa-
ter [16].
The water availability for irrigation is not a major is-
sue in the ORIA since relatively secured supplies are
likely to be available in the future for existing Stage 1
and future Stage 2 irrigation areas. In the ORIA an effi-
cient on-farm irrigation water management through an
optimal irrigation scheduling is mainly required to
maximise crop production and minimise excessive deep
drainage. Deep drainage fluxes can vary from negative
flux [17] to 119 mm per year [18] under irrigated sugar-
cane. Reduction in deep drainage fluxes through an op-
timal irrigation scheduling will help control rising wa-
tertables and the development of soil salinity in ORIA.
3. Model Description
The LEACHC version of LEACHM was selected for
irrigation scheduling and assessing the impacts of various
fresh and saline shallow watertables on soil salinity built
up when the maize and sugarcane are grown on the
Cununurra Clay. This model has previously been used
for irrigation scheduling under saline shallow watertable
conditions [7] and [8]. LEACHM (Leaching Estimation
And CHemistry Model) is one of the more complex and
comprehensive models for simulating processes in crop
root zones [19]. It can also be categorised as a complex
model with respect to its approach to soil chemistry be-
cause it considers the independent movement of individ-
ual ions, including equilibrating the soil solution phase
with the solid phase using precipitation-dissolution of
lime and gypsum, significant ionic-pairing, and cation
exchange. However it tends to under predict reactive
ions.
LEACHC uses a finite-difference solution of the
one-dimensional Richard's equation for unsaturated flow.
To approximate the hydraulic conductivity, matric poten-
tial and moisture content (K-h-θ) relationships, the model
uses either the expressions developed by [20] or fits the
two-part retentivity functions developed by [21]. If this
retentivity function is selected, various regression equa-
tions are available [19,22,23] and [24]. In this study the
equations developed by [20] were used for estimating
soil retention relationships based on input of soil textural
properties, bulk density, organic carbon and saturated
hydraulic conductivity in various layers of the soil profile.
To approximate evapotranspiration the model uses the
method of [25]. From the input of weekly pan evapora-
tion totals (P), the model calculates daily potential
evapotranspiration (ETd). To determine daily potential
transpiration (Td), ETd is multiplied by the crop cover
fraction (Ccf). The equation developed by [26] was used
to approximate the crop cover during various growing
stages of the maize and sugarcane. The daily potential
surface evaporation (Ed) is the difference between ETd
and Td. The equations used for maize and sugarcane root
growth and root density distribution as a function of time
in this study are based on those given by [27]. The water
uptake rate by the maize and sugarcane roots is approxi-
mated by using equation developed by [28].
A number of upper and lower boundary conditions are
provided in the model. The upper boundary conditions
include ponded or non-ponded infiltration and evapora-
tion or zero flux. The five different lower boundary con-
ditions are: a) fixed watertable depth; b) free draining
profile; c) zero flux; d) lysimeter tank; and e) fluctuating
watertable. A fixed watertable boundary condition was
used for this study. Use of Richard's equation for unsatu-
rated flow assumes that the soil is: homogeneous hori-
zontally, rigid and incompressible, non-hysteretic and
iso-thermal, and that there is no preferential flow.
After the solution of Richard's equation for unsaturated
flow, including sinks, the movement and distribution of
solutes are modelled by solving numerically the convec-
tion-diffusion equation (CDE). The model can handle the
movement and distribution of Ca, Mg, Na, K, Cl, SO4,
CO3, HCO3, H, OH and their major ion pairs.
4. Material and Methods
Two experimental sites (Figure 2) were selected to col-
lect the field data about soil physical properties; irriga-
tion frequency and application amounts; soil moisture,
watertable depth, and soil and water chemistry. The pur-
pose was to monitor temporal changes in soil moisture
and salinity profiles over the growing period to assess
any water or salt stress under current irrigation practice
and calibrate LEACHC to enable its use for evaluating
various irrigation management strategies.

Modelling Irrigation and Salinity Management Strategies in the Ord Irrigation Area 39
4.1. Kimberly Research Station Site–KRS 7A
This 6.6 ha site was located near the Kimberly Research
Station (KRS) in block 7A (Figure 2). The soil in this
block belongs to the Cununurra Clay and a maize crop
grown during 2004 was selected for the study. To deter-
mine the soil physical and textural properties, soil sam-
ples were collected from two locations (7A-1 and 7A-2).
At each location, the soil samples were collected from
various segments up to 2 m depth. Each soil sample was
analysed for soil texture, bulk density, organic carbon,
and soil moisture. The saturated paste extracts of soil
samples were analysed in the laboratory for major ions,
EC and pH. Soil textural and chemical data were used as
the initial soil moisture and soil chemical compositions
during model calibration. Textural properties of the soil
at KRS 7A-1 and KRS 7A-2 (Table 1) were averaged
and used as input in the LEACHC model for estimating
the soil retention properties. A total soil profile depth of
2 m was divided into 10 segments of 200 mm each seg-
ment. The soil textural properties varied across its vari-
ous segments as listed in Table 1 and so were the esti-
mated retention properties. The soil retention properties
estimated by the model were similar to those determined
through laboratory experiments by [29] for the
Cununurra Clay (Table 2). The amount of soil water
available for extraction by the maize plant roots from
various segments of a 2 m soil profile was averaged
around 220 mm.
Additional soil samples, collected on June 11, 2004,
July 12, 2004 and October 08, 2004, were analysed for
soil moisture and soil chemical properties (EC and pH)
and then compared with model predictions during the
model calibration. The watertable in the experimental
block, monitored by taking regular water level readings
from an existing bore hole at this site, varied around 4 m
below ground surface throughout the growing season and
accordingly its depth was fixed at 4 m in the model. A
groundwater sample, collected from the bore hole and
analysed for major ions, EC and pH, indicated that the
shallow groundwater was saline; EC around 400 mS/m
(Table 3); major ions were used as input in the model to
represent the initial chemical composition of the watert-
able.
The maize was sown during the last week of April
2004 which germinated during the first week of May and
developed its full canopy during the last week of July
2004. It was harvested during the first week of October
2004. These dates were used for simulating the maize
crop growth in the LEACHC. The fertilizer application
rates were 250 kg/ha Di-Ammonium Phosphate (DAP),
50 kg/ha Zinc Sulphate Monohydrate, 50 kg/ha Sulphate
of Potash and 460 kg/ha Urea. The maize roots can de-
velop up to 2 m below the ground surface [30]. However
most of the maize roots are concentrated within the top
parts of the soil profile according to many researchers
[31,32] and [33]. For this study a 2 m root zone was as-
sumed. The relative fraction of maximum root length
density followed that described by [34]. Using this ge-
neric distribution the root zone was subdivided into four
quarters with 40, 30, 20 and 10 percent of the roots in
each quarter starting from top of the soil profile.
Total irrigation and rainfall amounts applied to the
crop and used as input into the model were 1300 mm and
15 mm, respectively during the growing season (Table 4).
The irrigation applications remained uniform throughout
the growing period. During each watering, 9.5 ML was
applied in 12 hours to irrigate 6.6 ha of the maize. Because
Table 1. Soil textural properties at KRS 7A-1 and KRS 7A-2 near Kimberley Research Station.
KRS 7A-1
Depth (mm) Sand % Silt % Clay % OC* % KRS 7A-2
Depth (mm) Sand %Silt % Clay % OC* %
0-100 39.2 16.0 44.8 1.1 0-100 35.9 9.7 54.4 1.0
100-200 39.8 10.1 50.1 0.8 100-200 39.2 10.7 50.1 0.8
200-400 36.6 14.9 48.5 0.5 200-400 35.2 12.4 52.4 0.7
400-800 32.5 15.1 52.4 0.4 400-800 38.6 13.1 48.3 0.4
800-1100 36.3 14.2 49.5 0.4 800-1100 36.3 14.4 49.3 0.4
1100-1500 29.6 18.7 51.7 0.3 1100-1500 42.1 14.7 43.2 0.2
1500-1700 31.2 16.4 52.4 0.2 1500-1700 36.7 19.8 43.5 0.8
1700-2000 41.1 16.0 42.9 0.1
*Organic carbon.

Modelling Irrigation and Salinity Management Strategies in the Ord Irrigation Area 40
Table 2. Soil retention propertie s of cununurra clay.
Pore water pressure (bars)
0.001 0.1 0.33 0.67 1 3 15
Depth (mm) Bulk density
(g/cm3)
Water content (cm3/cm3)
0-100 1.40 0.42 0.36 0.32 0.31 0.31 0.27 0.17
100-200 1.42 0.41 0.35 0.32 0.31 0.31 0.27 0.20
200-300 1.44 0.39 0.35 0.33 0.32 0.32 0.28 0.22
300-400 1.48 0.41 0.37 0.34 0.33 0.32 0.29 0.23
400-500 1.52 0.45 0.40 0.37 0.35 0.34 0.30 0.24
500-600 1.51 0.43 0.39 0.36 0.34 0.34 0.30 0.25
600-700 1.51 0.43 0.40 0.36 0.35 0.34 0.30 0.26
700-800 1.52 0.45 0.41 0.38 0.36 0.36 0.31 0.27
800-900 1.52 0.47 0.42 0.39 0.37 0.36 0.32 0.27
900-1200 1.52 0.45 0.41 0.38 0.36 0.36 0.31 0.28
1200-1500 1.52 0.47 0.42 0.39 0.37 0.36 0.32 0.28
1500-2000 1.52 0.47 0.42 0.39 0.37 0.36 0.32 0.28
Table 3. Groundwate r and irrigation w a ter quality.
Ca Mg Na K Cl S HCO3 EC pH TDS
mg/L mS/m Mg/L
Groundwater quality at KRS-7A 68 104 495 3 960 19 475 400 7.78 -
Groundwater quality at CUM55 13 16 28 3 27 3 123 46 7.89 -
Irrigation water quality (Diversion Dam) 25 12 20 3 14 3 183 30 8.06 178
Table 4. Irrigation and rainfall amounts for the Maize crop at KRS-7A.
Date 02/05/04 13/05/04 26/05/04 03/06/04 12/06
Irrigation/rainfall (mm) 144 144 144 13.5* 144
Date 27/06/04 14/07/04 25/07/04 07/08/04 19/08/04
Irrigation/rainfall (mm) 144 144 144 144 144
*Denotes rainfall. Only rainfall amounts of 10 mm or more were used in the model
irrigation water quality was not expected to change in the
short term, only three irrigation water samples were col-
lected during cropping season and analysed for major
ions, EC and pH. These values were used as input to
represent the irrigation water quality in the LEACHC
model (Table 3). The weather data were obtained from
KRS weather station. The daily pan evaporation and
temperature data were used to determine the weekly total
pan evaporation and mean weekly temperatures and the
amplitudes. Figure 3 shows the weekly total pan evapo-
ration and maximum and minimum temperature data
from KRS weather station between April 2004 and July
2005. For the maize crop these data between April and
ctober 2004 were used. O

Modelling Irrigation and Salinity Management Strategies in the Ord Irrigation Area41
Figure 3. Total weekly pan evaporation and mean weekly minimum and maximum temperatures at KRS during 2004 and 2005.
The saturated past extracts of the soil samples were
analysed in the laboratory for the chemical analysis. The
major ions obtained from this analysis of the soil samples
collected from KRS 7A-1 and KRS 7A-2 were averaged
and used as input in the model to represent the initial
chemical composition of the soil profile (Table 5). Al-
though LEACHC can handle the movement of all major
ions but for this study only EC values as representative
of overall salinity, obtained from analysis of the soil
samples collected during and after the growing period,
were used for model calibration.
4.2. Cummings Farm Site-CUM 55
The second site was selected at Cummings farm in block
55 (CUM 55), which has soil type belonging to the
Cununurra clays (Figure 2). Sugarcane grown on this
block during 2004-05 was selected for the study. To de-
termine the soil physical and textural properties, initial
soil samples were collected from various segments of the
soil profile up to 2 m depth at two locations (CUM 55-1
and CUM 55-2) immediately before the start of the
growing season. The samples were analysed for soil tex-
ture, bulk density, organic carbon, soil moisture and soil
chemistry (Table 6). These properties at the two loca-
tions (CUM55-1 and CUM55-2) were averaged and used
as input to the LEACHC model for estimating the soil
retention parameters. The total amount of soil water
available for extraction by the sugarcane plant roots from
various segments of a 2 m soil profile was averaged
around 210 mm.
Table 5. Major ions used as initial chemical composition of the soil profile for KRS site (maize crop).
Major ions (mg / L)
Depth (mm) Na Mg S Cl K Ca HCO3 pH
0-100 2.9 3.9 20.6 44.9 0.2 4.7 70.2 6.8
100-200 2.4 1.1 12.6 32.3 0.2 1.0 103.2 7.4
200-400 3.5 0.8 16.9 64.0 0.2 0.7 128.0 7.9
400-800 9.2 0.7 29.0 230.0 0.2 0.6 160.3 8.2
800-1100 15.7 0.8 53.8 414.0 0.2 0.6 157.9 8.3
1100-1400 24.6 1.4 89.9 701.5 0.2 1.0 126.9 7.8
1400-1700 12.6 0.6 56.0 272.0 0.2 0.2 186.5 8.3
1700-2000 10.1 0.5 31.1 227.2 0.2 0.1 196.5 8.4

Modelling Irrigation and Salinity Management Strategies in the Ord Irrigation Area 42
Table 6. Soil textural properties in Block 55 at CUM 55-1 and CUM 55-2 at Cummings farm.
CUM 55-1 CUM 55-2
Depth (mm) Sand % Silt % Clay % OC* % Depth (mm)Sand % Silt % Clay % OC* %
0-100 26.0 10.6 63.4 1.12 0-100 23.1 19.3 57.6 0.93
100-200 25.3 16.0 58.7 1.04 100-200 27.2 8.1 64.7 1.16
200-400 27.3 11.5 61.2 0.60 200-400 25.0 14.6 60.4 0.42
400-800 23.4 17.7 58.9 0.47 400-800 23.6 13.6 62.8 0.41
800-1100 22.9 18.8 58.3 0.43 800-1100 25.9 12.2 61.9 0.41
1100-1500 22.2 22.0 55.8 0.53 1100-1500 24.0 13.4 62.6 0.32
1500-2000 22.2 34.1 43.7 0.22 1500-1700 54.9 12.3 32.8 0.15
Additional soil samples, collected on June 11, 2004,
July 12, 2004, October 08, 2004, March 12, 2005 and
July 01, 2005, were analysed for soil moisture and soil
chemical properties (EC and pH) and compared with the
simulated data during model calibration. The watertable
in the experimental block varied between 3.8 and 4.2 m
below ground surface during the simulation period. It
was assumed at a fixed depth of 4 m in this study. A
groundwater sample, collected from the bore hole, and
analysed for major ions, EC and pH, indicated that the
shallow groundwater quality was relatively fresh with EC
46 mS/m (Table 3); major ions were used as input into
LEACHC to represent the initial chemical composition
of the watertable. A sugarcane crop was planted during
second week of May 2004. It emerged from the ground
during the fourth week of May 2004. The crop developed
full canopy by the second week of August 2004 and its
harvest started during the last week of June 2005. Fertil-
izer application rates were 250 kg/ha DAP, 10 kg/ha Zinc,
15 kg/ha Sulphur and 325 kg/ha Urea. These dates were
used in the model to simulate the sugarcane crop growth
in the model. A rooting depth of 2 m was assumed for the
sugarcane crop. The root zone of 2 m was subdivided
into four quarters with 40, 30, 20 and 10 percent of the
roots in each quarter starting from top of the soil profile.
About 1900 mm (946 ML) was applied through 14 irri-
gations and around 700 mm was received from rainfall
during the growing season (Table 7). The irrigation ap-
plication amounts varied between 106 and 168 mm (53-
84 ML applied as one irrigation to the 50 ha crop). These
irrigation application and rainfall data were used as input
in the model. The chemical composition of the irrigation
Table 7. Irrigation and rainfall amounts for the Sugarcane crop at CUM 55.
Date 08/05/04 03/06/04 20/06/04 22/07/04 17/08/04 24/09/04 28/10/04
Irrigation/rainfall* (mm) 168 10.5* 120 148 120 120 168
Date 06/11/04 08/11/04 13/11/04 15/11/04 22/11/04 09/12/04 09/12/04
Irrigation/rainfall (mm) 12.5* 15* 106 21.5* 106 144 144
Date 22/12/04 26/12/04 27/12/04 01/01/05 02/01/05 03/01/05 06/01/05
Irrigation/rainfall (mm) 16.7* 13.4* 43.1* 17* 130.2* 42.8* 14*
Date 12/01/05 13/01/05 18/01/05 20/01/05 31/01/05 03/02/05 15/02/05
Irrigation/rainfall (mm) 20* 13* 168 24.6* 28.8* 34.1* 140
Date 06/03/05 16/03/05 17/03/05 04/04/05 18/04/05 05/05/05
Irrigation/rainfall (mm) 33* 52.6* 74* 120 144 120
*Denotes rainfall. Only rainfall amounts of 10 mm or more were used in the model.

Modelling Irrigation and Salinity Management Strategies in the Ord Irrigation Area43
water (Table 3) was used to represent irrigation water
quality. The weekly pan evaporation and maximum and
minimum temperature data from KRS weather station
between April 2004 and July 2005 were used as input in
the LEAHC model for calibration of the sugarcane crop
(Figure 3).
The soil samples collected from various depth seg-
ments at CUM55-1 and CUM55-2 before the start of
growing season of the sugarcane crop were analysed for
chemical composition. The chemical composition at each
depth segment from two locations was averaged and used
as input in the LEACHC to represent the initial chemical
composition of the soil profile (Table 8). The EC values
obtained from analysis of the soil samples collected dur-
ing and after the growing period were used for model
calibration.
5. Results and Discussion
5.1. Model Calibration
5.1.1. Maize Crop at Kimberly Research Station
Site—KRS 7A
To calibrate the LEACHC model for the maize crop,
field data about soil textural, physical and chemical
properties, crop growth, irrigation amounts and quality,
watertable depth and quality, total weekly pan evapora-
tion, mean weekly temperatures, and watertable depth
and quality were used. The total soil profile depth con-
sidered for modelling was 2 m with 20 segments of 100
mm each. The simulation started on 01/04/04, about one
month before the crop sowing date, to enable equilibra-
tion of soil moisture in the soil profile before the start of
the growing season. The simulation end date was on No-
vember 30, 2004, approximately 20 days after the crop
was harvested on October 07, 2004.
Soil samples, collected during the growing season and
analysed for soil moisture and soil chemistry, were used
for comparison with the model results. The two parame-
ters (α and β) in Campbell’s equation [20] were slightly
adjusted to achieve a reasonable agreement between the
observed and predicted soil moisture content and salinity
profiles. The comparison between the observed and pre-
dicted soil moisture content at three dates; April 27, 2004,
July 12, 2004 and October 08, 2004 shows that the
agreement between observed and predicted soil moisture
was reasonable except in the top layers of the soil profile
where the model over-predicted the soil moisture content
(Figure 4). The Willmott’s d-index [35], a measure of
the degree of agreement between the observed and pre-
dicted values, was above 0.5. Given the inherent diffi-
culty in estimating the soil retention properties in various
layers by either using Campbell’s equation or various
regression equations, this level of agreement between the
observed and predicted water content was viewed as suf-
ficiently accurate for simulating the soil moisture in vari-
ous irrigation management scenarios.
The predicted soil profile electrical conductivity (EC)
at the predicted moisture content was converted to ECe
(electrical conductivity of the saturated paste extract)
based on field and saturated water content [36] for a
meaningful comparison with the observed ECe. The ECe
will be termed as EC hereafter for simplicity. The
LEACHC tends to under predict reactive ions according
to [7] and [8]. For this reason the comparison of the ob-
served and modelled reactive ions was not included in
the study. Comparison of the observed and predicted soil
profile EC at three different dates shows that the agree-
ment between the observed and predicted EC was good
except in one layer located just below the middle of the
soil profile where it was under-predicted by the model
(Figure 5). There was an unusual sudden increase
Table 8. Major ions used as initial chemical composition of the soil profile for CUM55 site (Sugarcane crop).
Major ions (mg / L)
Depth (mm) Na Mg S Cl K Ca HCO3
pH
0-100 4.9 2.6 18.0 336 0.2 3.1 80.0 7.4
100-200 3.4 3.2 18.0 324 0.1 4.1 27.4 7.0
200-400 5.1 5.1 21.4 558 0.1 7.8 45.8 6.8
400-800 8.8 7.8 37.2 854 0.1 12.1 56.1 7.0
800-1100 14.4 10.2 48.1 1230 0.2 14.3 70.8 7.2
1100-1400 16.3 11.9 54.8 1418 0.2 17.0 95.2 7.7
1400-1700 9.3 4.7 29.5 678 0.2 6.4 80.2 7.5
1700-2000 30.7 13.6 78.1 2037 0.3 20.4 98.7 7.6

Modelling Irrigation and Salinity Management Strategies in the Ord Irrigation Area 44
Figure 4. Comparison of the observed and predicted soil profile water content at KRS 7A on 27/04/04 (left), 12/07/04 (middle)
and 08/10/04 (right).
Figure 5. Comparison of the observed and predicted soil profile EC at KRS 7A on 27/04/04 (left), 12/07/04 (middle) and
8/10/04 (right). 0

Modelling Irrigation and Salinity Management Strategies in the Ord Irrigation Area 45
Comparisons between the observed and predicted soil
moisture contents, depicted in Figure 6 for three dates
during 2004 and Figure 7 for two dates during 2005
show that the agreement between the observed and pre-
dicted water content was good except on July 08, 2004,
when the model under-predicted the soil moisture content
in the middle part of the profile which may be due to the
use of an incorrect irrigation event in the model. As ex-
pected, both the observed and predicted water contents in
the surface layers were relatively drier. In the remaining
profile the water content was relatively uniform at most
dates. The Root Mean Square Error (RMSE) ranged be-
tween 0.01 and 0.03 in various segments of the soil pro-
file. The Willmott’s d-index, a measure of goodness of fit,
ranged between 0.45 and 0.55, which was reasonable
considering the variation of soil structure and physical
properties expected in the various soil layers.
in the observed EC of this layer at two dates that can not
be explained. However, in general, the model did a very
good job of predicting the soil profile EC. The Willmot’s
d-index, a measure of the level of agreement between the
observed and predicted EC, was averaged around 0.5.
The inability of the model to accurately predict EC of the
middle soil layers at one occasion (October 2004) re-
sulted in a lower average d-index. A reasonable agree-
ment between the observed and predicted water content
and salinity data suggested that this calibrated model can
be used to simulate both water content and salinity pro-
files for the maize crop grown on the Cununurra clay.
5.1.2. Su ga rcane Crop at Cummings Site—CUM 55
To calibrate the LEACHC model for the sugarcane
grown on Cununurra clay required field data collected
from CUM 55 and climate data from KRS weather sta-
tion were used. The simulated depth and depth segments
were the same as for the maize. The simulation started on
April 01, 2004, about one month before sowing to enable
equilibration of soil moisture in the soil profile before the
start of the growing season and ended on July 31, 2005,
approximately 40 days after the crop was harvested on
June 22, 2005. Slight adjustments to the two parameters
(α and β) of Campbell’s equation [20] were made to
achieve a reasonable agreement between the observed
and predicted soil moisture content and salinity profiles.
Comparison between the observed and predicted soil
profile ECs on the three dates during 2004 and two dates
during 2005 shows that the agreement between the ob-
served and predicted EC was reasonable (Figure 8 and
Figure 9). At some dates (July 08, 2004, March 12, 2005
and July 01, 2005) the model slightly over-predicted EC
in the middle layers. The prediction was relatively good
in the lower layers of the soil profile at most dates. The
predicted EC also was close to the observed EC in the
top layers of the soil profile except on April 27, 2004,
Figure 6. Comparison of the observed and predicted soil profile soil moisture content during 2004 on 27/04/04 (left), 11/06/04
(middle) and 08/07/04 (right).

Modelling Irrigation and Salinity Management Strategies in the Ord Irrigation Area 46
Figure 7. Comparison of the observed and predicted soil moisture content of the soil profile at CUM 55 on 12/03/05 (left) and
01/07/05 (right).
Figure 8. Comparison of the observed and predicted soil profile EC at CUM 55 on 27/04/04 (left), 11/06/04 (middle) and
08/07/04 (right).

Modelling Irrigation and Salinity Management Strategies in the Ord Irrigation Area 47
Figure 9. Comparison of the observed and predicted soil profile EC at CUM 55 on 12/03/05 (left) and 01/07/05 (right).
when it was over-predicted, and July 01, 2005 when it
was under-predicted.
A reasonable agreement between the observed and
predicted water content and salinity values suggested that
the calibrated model is able to simulate soil moisture
content and salinity profiles reasonably well if it is used
to devise irrigation management strategies for the sugar-
cane crop grown on the Cununurra clay soil for various
watertable depths and salinity conditions.
5.2. Model Application
5.2.1. Irrigation Scheduling of Maize Crop—Deep
Watertable
To evaluate various irrigation management strategies for
the maize crop grown on Cununurra clay all together six
simulations, three irrigation application amounts and two
irrigation intervals, were considered. In the first three,
irrigation application amounts equal to 100%, 75% and
50% of the total fortnightly pan evaporation from the
past 14 days were applied every fortnight as irrigation.
These simulations will be called IPF100ET, IPF75ET
and IPF50ET, where IP stands for irrigation practice, F
represents a fortnightly irrigation interval, and 100ET
indicates the percent of total fortnightly pan evaporation
applied as irrigation. In the other three simulations, the
irrigation interval was changed from 14 days to 7 days
during the second half of the growing season. Weekly
irrigation application amounts equal to 100%, 75% and
50% of the total weekly pan evaporation from the past 7
days were applied every week as irrigation. These simu-
lations will be called IPM100ET, IPM75ET and
IPM50ET, where M indicates a mixed irrigation interval
of 14 days during the first half of the growing season and
7 days during second half. The model simulation using
the actual observed irrigation data was termed as CIP
(current irrigation practice).
Total irrigation and rainfall application was largest
under CIP and lowest under IPF50ET (Figure 10). The
ET was maximum under IPF100ET and lowest under
IPF50ET. The total amount of water used as ET in the
IPF100ET (825 mm) and IPF75ET (771 mm) was sig-
nificantly higher than CIP (740). The maximum ET will
therefore be likely if IPF100ET is adopted as irrigation
practice. In fact, both IPF100ET and IPF75ET seem at-
tractive with respect to total ET. The runoff and drainage
losses were highest under CIP and lowest under IPF50ET
(Figure 10). The total water lost as runoff under
IPF100ET (255 mm) was significantly lower than that
under CIP (640 mm). It was much lower for both
IPF75ET and IP50ET than CIP. The same was true for
the total amount lost to drainage (Figure 10). Most of the
extra water applied as irrigation under CIP was either lost
as runoff or drainage. In addition to evaluation of the
distribution of total applied irrigation water into ET, run-
off and drainage the availability of soil moisture between
irrigations and the impacts on soil salinity were also as-
sessed to enable the selection of an optimal irrigation
strategy.

Modelling Irrigation and Salinity Management Strategies in the Ord Irrigation Area 48
Figure 10. Water balances for the simulated irrigation op-
tions at KRS 7A; proportions of total applied irrigation
water, ET, runoff and drainage.
The soil moisture profiles for CIP, IPF100ET, IPF75ET
and IPF50ET, shown in Figure 11, represent the soil
moisture content on the day before each irrigation date
for seven irrigation events of the maize crop. The soil
moisture content on the day before irrigation for the re-
maining four irrigation events, not shown in Figure 11,
was above allowable depletion (AD). The wilting point
and allowable depletion water content profiles also are
shown in this Figure. The allowable depletion water
content was assumed to be 0.5 of the total available water
between field capacity and wilting point. The predicted
soil moisture profiles for all irrigation strategies and
monitoring dates always remained above the wilting
point. Under CIP, the soil moisture content was less than
the allowable depletion on September 15, 2004 and Sep-
tember 30, 2004 (Figure 11). For IPF100ET, there were
three occasions when soil moisture in middle parts of the
soil profiles was less than the AD. For IPF75ET and
IPF50ET there were four and five occasions, respectively,
when the soil moisture profiles were lower than AD
(Figure 11). The soil moisture content was always above
AD during first half of the growing season because of
relatively small ET demand.
The soil moisture profiles of CIP were similar to
IPF100ET with respect to the water availability for the
crop. If it is assumed that the maize crop was already
mature during the month of September and ready for
harvest (no irrigation was applied during this month in
CIP) then the impact of the last two soil moisture profiles
on the crop water availability can be ignored; both of
these were less than the AD. Accordingly, the CIP may
appear to be the best irrigation practice with respect to
soil water availability but, as discussed earlier, it caused
the largest amount of wastage in the form of runoff and
drainage. The predicted amount of water used as ET in
the CIP also was less than that in the IPF100ET. The
IPF100ET is therefore a better strategy with respect to
both crop water availability and water saving. It would
require around 11 ML/ha and deliver a net saving of 330
mm (23%) over one growing season without any signifi-
cant crop water stress. The predicted water savings are
likely to be achieved from reductions in the runoff and
drainage. The IPF75ET would require around 8.4 ML/ha
and deliver a net saving of around 40%; however, the
crop would be under minor stress for a few days. This
water requirement of 8.4 ML/ha is slightly higher than
7.5 ML/ha determined by [44] for the same crop in
semi-arid tropical environments of Northern Territory
(Katherine, Douglas Daly, Dalywaters, Mataranka, and
Larrimah), Western Australia (Kununurra, Derby, and
Broome) and Queensland (Gordonvale). In IPM75ET the
level of stress was reduced by decreasing the irrigation
interval from fortnightly to weekly in last half of the
growing season (Figure 12). This resulted in wetter soil
profiles than those under the IPF75ET during second half
of the growing season. There was no significant build up
of soil salinity and differences in the predicted soil salin-
ity profiles over time among the various irrigation strate-
gies.
It is important to maintain water availability above AD
level especially during vegetative growth, flowering and
reproductive stage of the maize crop because of its sensi-
tivity to both water deficit and its timing. A significant
reduction in yield can occur due to water deficit during
both vegetative and reproductive period [37]. Both [37]
and [38] conclude that the water deficit during flowering
stage in particular has a devastating effect on maize yield.
The IPM75ET irrigation strategy maintains favourable
soil moisture conditions or water availability throughout
the growing season thus saving water as well as ensuring
an optimal yield.
Based on the above model predictions, it is concluded
that irrigation application equal to 100% of total fort-
nightly pan evaporation at 14 days interval is a better
irrigation strategy (IPF100ET) and would save around
23% water. An irrigation application amount equal to
75% of total fortnightly and weekly pan evaporation at
14 day interval during the first half of the growing season
and 7 day interval during the second half would be the
best irrigation option (IPM75ET) if it is practicable to
change the irrigation interval. This irrigation strategy
would save around 40% water.
5.2.2. Irrigation Scheduling of Sugarcane
Crop—Deep Watertable
The irrigation intervals and amounts used to irrigate sug-
arcane in the experimental block CUM 55 during
2004-05 were applied to simulate the current irrigation

Modelling Irrigation and Salinity Management Strategies in the Ord Irrigation Area 49
Figure 11. Soil moisture profiles the day before irrigations for the Maize crop at KRS 7A: CIP (top left), IPF100ET (top
right), IPF75ET (bot. left) and IPF50ET (bot. right).
practice (CIP). Five irrigation application strategies, in
addition to the CIP, were simulated to determine an irri-
gation schedule that would produce the maximum soil
moisture availability, minimum runoff and drainage,
maximum ET, and minimum salinity accumulation in the
soil profile.
The first irrigation strategy is IPF100ET as used for
maize. The second irrigation strategy (IPF75-100ET)
uses an irrigation amount equal to 75% of the total fort-
nightly ET from previous two weeks applied every fort-
night for the first quarter of the growing season and
100% of total fortnightly ET applied during the rest of
the growing season. In the third irrigation strategy
(IPF50-100ET) the irrigation amounts were 50% of the
total fortnightly ET during first quarter of the growing
season and 100% during rest. In the fourth irrigation
strategy (IPM50-100ET), an irrigation application
amount equal to 50% of total fortnightly ET was applied
every fortnight during first quarter of the growing season
and 100% of total weekly ET was applied every 7 days
during remainder of the growing season. The fifth irriga-
tion strategy (IPM50-75ET) was the same as the fourth,
except the irrigation amount was 75% of total weekly ET
during the final three-quarters of the growing season.
The total irrigation and rainfall amount was smallest
for IPM50-75ET, largest for IPF100ET, and was similar
for IPF50-100ET and IPM50-100ET (Figure 13). The
total ET was lowest under CIP and largest under
IPF100ET. It was significantly larger in all irrigation
strategies than CIP. Its variation between irrigation
strategies was small (2375-2265 mm), except CIP. The
model predicted the highest runoff under CIP and lowest

Modelling Irrigation and Salinity Management Strategies in the Ord Irrigation Area 50
Figure12. Soil moisture profiles the day before IPM75ET
irrigations for the Maize crop at KRS 7A.
Figure 13. Water balances for the simulated irrigation op-
tions at CUM 55; total applied irrigation amount, ET, run-
off and drainage.
under IPM50-75ET. The total drainage was largest under
IPF100ET and lowest under IPM50-75ET. Considering
total ET, runoff and drainage together, IPM50-100ET
and IPM50-75ET gave the best results; both had compa-
rable total ET and less runoff and drainage than CIP.
Assessments of the soil moisture availability and salt
accumulation in the root zone also are required to iden-
tify the best irrigation strategy.
The soil moisture profiles on the day before each irri-
gation for CIP (Figure 14) show that there were only few
dates when the soil moisture was above the allowable
depletion level indicating that the sugarcane crop was
under soil moisture stress. On some dates, the soil mois-
ture was very close to the wilting point, and it is expected
that the crop experienced moisture stress at least during
Figure 14. Soil moisture profiles the day before CIP irriga-
tions for the Sugarcane crop at CUM 55.
these days. A small total ET under CIP also indicates that
the crop was under moisture stress at least some days
during the growing season. Neither the irrigation
amounts nor the irrigation intervals were appropriate.
The irrigation amounts were large, which resulted in ex-
cessive runoff; and the irrigation intervals were too long,
which resulted in soil moisture stress.
Soil moisture stress or water deficits have varying ef-
fects on sugarcane crop development, biomass accumu-
lation and partitioning of biomass to millable stalk and
sucrose, both during the season and at final harvest [39].
Water deficits during the tillering phase significantly
affects leaf area, tillering and biomass accumulation but
have relatively little effects on final yield. However wa-
ter deficits, after the leaf area index is reached over 2,
have more deleterious effects on final yield of total bio-
mass, stalk biomass and stalk sucrose [39]. Therefore it is
highly likely that the water deficit occurred during the
growing season under CIP had significant impacts on
final yield of sugarcane. The predicted soil moisture pro-
files for the IPF100ET, IPF75-100ET, IPF50-100ET,
IMP50-75ET were below AD level on some dates. The
soil moisture profiles for IPM50-100ET, shown in Fig-
ure 15, were always above the AD level, except one date.
This irrigation strategy (IPM50-100ET) is recommended
for the sugarcane crop grown on the Cununurra clay in
the ORIA. This strategy ensures to maintain favourable
soil moisture conditions throughout the growing season
which is a prerequisite for the maximum crop productiv-
ity. A total of about 2200 mm of water will be required
for irrigation where crops are irrigated after half the soil
water supply is depleted. Thi amount equates to about s

Modelling Irrigation and Salinity Management Strategies in the Ord Irrigation Area 51
Figure 15. Soil water content profiles the day before IPM50-100ET irrigations for the Sugarcane crop at CUM 55; 13/04/04
(left) to 07/06/05 (right).
22 ML/ha. About 78% of the total applied water will be
used as ET, 12% will be wasted as runoff, and around
10% will be lost to drainage. This water requirement of
22 ML/ha is close to lower end of the range (22.7 to 23.8
ML/ha) estimated by [40] using the APSIM-sugarcane
model for the sugarcane crop grown over Cununurra clay
in ORIA. According to [41] the observed and modelled
water requirement of the sugarcane crop in the Burdekin
Delta, located on the dry-tropical coastal strip in North
Queensland, was 20.5 to 20.3 ML/ha which is only
slightly lower than 22 ML/ha estimated in this study.
This comparison shows findings from this study are
similar to those by [40] and [41]. This also confirms the
suitability of the LECHC for irrigation scheduling of
crops with an added advantage of salinity modelling. The
salinity modelling for IPM50-100ET suggested that there
was salt accumulation over time in some parts of the soil
profile however, the accumulation was well below the
threshold (170 mS/m) that would affect sugarcane crop
productivity.
5.2.3. Irrigation Scheduling of Sugarcane
Crop—Non-Saline Shallow Watertables
The calibrated LEACHC model was used to assess the
impacts of non-saline shallow watertables on irrigation
water requirements, irrigation scheduling and soil salinity
risks. Two shallow watertable depths (1 and 2 m) with
EC of 50 mS/m were considered in the modelling. It was
assumed that a sugarcane crop grown on Cununurra clay
was present throughout a total simulation period of three
years. For each watertable depth, four simulations were
conducted; IPF75ET, IPF50ET, IPW75ET and IPW50ET,
where F indicates a fortnightly irrigation interval, as
above, and W indicates a weekly irrigation interval. Thus,
IPW75ET denotes that the irrigation interval was seven
days and the irrigation application amount was 75% of
total fortnightly pan evaporation (ET). One year (May
2004 to April 2005) of pan evaporation and temperature
data, obtained from the KRS weather station, were re-
peated in the subsequent two years of simulation. The
model predicted significant ET contributions from the
two shallow watertables; the shallower the watertable the
greater the groundwater contribution to ET for a particu-
lar irrigation strategy (Figure 16). The groundwater con-
tribution was maximum (60% of the total ET) from a 1 m
deep watertable for IPF50ET and was minimum (26% of
total ET) from a 2 m watertable for IPW75ET. Accord-
ing to [43] a 1 m deep watertable in a sandy loam soil
provided 65% of sugarcane ET in India which is similar
to that estimated in this study under IPF50ET. The study
by [42] concluded that nearly all ET requirements of the
sugarcane crop grown on light medium and medium clay
or sandy loam soil in Australia can be met from watert-
able if it is within 1 m of the soil surface. This study con-
firms the findings from other studies conducted in Aus-
tralia [42] and elsewhere [43] and suggests that at least

Modelling Irrigation and Salinity Management Strategies in the Ord Irrigation Area 52
Figure 16. Predicted groundw ater contributions to total ET
for Sugarcane crop grown over non-saline (50 mS/m) 1 and
2 m deep watertables.
60% of ET requirements of the sugarcane crop can be
met from shallow watertables which is not only a sub-
stantial water saving but also helps control watertables.
The predicted average soil EC profiles for both wa-
tertable depths increased slightly over time (Figure 17).
The variation in the predicted average EC profiles among
the simulated irrigation strategies and between two wa-
tertable depths was small. Although the average soil pro-
file EC increased during the simulation period from the
initial levels it remained well below the threshold for any
adverse impacts on the sugarcane crop.
Based on the simulated results, the IPF50ET irrigation
strategy is recommended for sugarcane crops for non-saline
shallow watertables of one to two m depth. The model
predicted that this irrigation strategy will result in the
maximum irrigation water use efficiency because a
greater proportion of shallow groundwater is used for ET
requirements. The model also predicted that this irriga-
tion strategy will cause the accumulation of salts in the
root zone during the simulation period but well below the
threshold for any adverse impacts on the crop yield.
5.2.4. Irrigation Scheduling of Sugarcane
Crop—Saline Shallow Watertables
The same eight simulations, as above for the non-saline
watertables, were conducted except that the shallow wa-
tertables were assumed saline. In the first four, a saline
watertable with an EC of 200 mS/m was fixed at 1 m
depth. In the second four, the watertable was fixed at 2 m
depth with an EC of 300 mS/m. The initial average soil
profile EC was around 55 mS/m.
There was no significant difference between ground-
water contributions to ET for crops grown over saline
and non-saline watertables. The soil moisture availability
between irrigations was similar in all irrigation strategies
and was always above the AD level. The use of saline
groundwater for ET requirements resulted in salt accu-
mulation in the soil profile and average EC of the soil
profile increased significantly over time for all irrigation
strategies and watertable depths. At both watertable
depths the predicted average soil profile EC over time
was largest (> 900 mS/m) for irrigation strategy IPF50ET
(Figure 18 and 19). Low irrigation application caused
Figure 17. Predicted average soil profile EC for Sugarcane crop gr own over a non-saline (50 mS/m) 1 m deep watertable.

Modelling Irrigation and Salinity Management Strategies in the Ord Irrigation Area53
Figure 18. Predicted average soil profile EC for Sugarcane crop grown over a saline (200 mS/m) 1 m deep watertable.
Figure 19. Predicted average soil profile EC for Sugarcane crop grown over a saline (300 mS/m) 2 m deep watertable.
withdrawal of more water from the watertable which
resulted in the highest average soil profile EC over time.
The lowest average soil profile EC (> 500 mS/m) re-
sulted from IPW75ET at both watertable depths but it
was well above the level tolerable by the sugarcane crop.
In summary, the modelling suggests that the soil pro-
file salinity risk will be high if a saline watertable exists
for long periods at or above 2 m depth which is consis-
tent with an earlier finding by [8]. Over irrigations may
reduce the build up of soil profile salinity through flush-
ing but it will result in excessive accessions to the wa-
tertable causing groundwater to rise even further. The
recommended management strategy for a saline shallow
watertable is to lower its level below 2 m by artificial
deep open or sub-surface drainage first and then apply
regular leaching applications to flush excessive salts
from the root zone area into the drainage system [8].
Without this intervention, it is likely that high evapora-
tive demands, extended fallow periods and low irrigation
application will cause excessive accumulation of salts in
the soil profile.
5.3. Application of Recommended Irrigation
Strategies
This study identified irrigation strategies that ensure effi-
cient water use, optimal crop water availability and mini-

Modelling Irrigation and Salinity Management Strategies in the Ord Irrigation Area 54
mal salinity risks for the maize and sugarcane crops in
the ORIA. The practical application and feasibility of the
recommended irrigation strategies including irrigation
intervals and their variation within a growing season
were confirmed through discussions with the Ord Irriga-
tion Cooperative and farming community. New irrigation
areas are being developed under Ord Stage 2, adjacent to
the existing Stage 1 irrigation area, where large areas are
likely to be allocated for sugarcane. Because of similar
climate, soil and other conditions, the irrigation strategies
identified for the sugarcane crop in the Stage 1 area will
be applicable to this crop to be grown in Stage 2 area.
Also because the Ord Stage 2 is largely a closed ground-
water flow system it will be even more important to con-
trol deep drainage in this area. Any deep drainage in ex-
cess of irrigation requirements is likely to cause a rise in
groundwater levels and increase the risk of soil salinity
development.
Water resource availability for irrigation of the exist-
ing and new irrigated areas in the Ord is not likely to be a
major issue as enough water resource is expected to be
available to meet the current and likely future water de-
mands by the irrigation industry. The practical applica-
tion of the preferred irrigation techniques is therefore less
important with respect to water saving in the ORIA and
more important for achieving an optimal yield and con-
trolling or reducing deep drainage especially under Ord
Stage 2 to avoid the development of shallow watertables
and soil salinity. An inefficient irrigation strategy that
allows excessive deep drainage in Ord Stage 2 in par-
ticular will necessitate the installation of subsurface
drainage systems to control rising watertables if crop
productivity is to be maintained. Such drainage installa-
tions, whether open deep drains or subsurface systems
often require significant investments and have associated
problems of safe disposal of drainage waters.
The irrigation water requirements assessed using
LEACHC were compared with findings from other stud-
ies to test the applicability of the LEACHC model for
irrigation scheduling and salinity management and ex-
tending the results to other regions in Australia. The wa-
ter requirements assessed in this study were similar to
those of the maize crop estimated by [44] at Kununurra
(Ord), Derby and Broome in Western Australia; Kathe-
rine, Douglas Daly, Dalywaters, Mataranka, and Larri-
mah in Northern Territory; and Gordonvale in Queen-
sland. This confirms both the applicability of these find-
ings to other regions of Australia and the suitability of
LEACHC for such a purpose. Similarly the sugarcane
water requirements of 20.5 ML/ha to 23 ML/ha deter-
mined by [40] in the Ord and by [41] in the Burdekin
Delta are similar to the water requirements of 22 ML/ha
determined here. It means that the water requirements of
the maize and sugarcane crops are similar in the Austra-
lian semi-arid tropical environments and therefore the
irrigation water requirements determined in this study are
applicable in these other environments of Australia.
6. Conclusions
This study found that the irrigation application amounts
equal to 100% of the total fortnightly pan evaporation,
applied at 14 days interval, would be a better irrigation
strategy for maize crop grown on Cununurra clay over a
deep watertable. The predicted irrigation water use
would be around 23% less than the exiting practice. Irri-
gation application amounts equal to 75% of the total
fortnightly pan evaporation, applied every fortnight dur-
ing first half of the growing season, and 75% of the total
weekly pan evaporation, applied every week during the
second half of the growing season, would be the best
irrigation strategy if it is feasible to change the irrigation
interval from 14 days to 7 days. The irrigation water use
for this irrigation strategy was predicted to be around
40% less than the existing irrigation practice.
The study found that the best irrigation strategy for the
sugarcane crop grown on Cununurra clay over a deep
watertable would be irrigation application amounts equal
to 50% of the total fortnightly pan evaporation, applied
every fortnight during first quarter of the growing season,
and irrigation application amounts equal to 100% of total
weekly pan evaporation, applied every week during rest
of the season. This irrigation strategy would require
around 22 ML/ha of irrigation water for a single sugar-
cane crop.
The best irrigation strategy for the sugarcane crop
grown over a non-saline shallow watertable of ≤ 2 m
depth would be irrigation application amounts equal to
50% of the total bi-weekly pan evaporation, applied
every 14 days. The model predicted that this irrigation
strategy would result in the best water use efficiency by
encouraging plants to use groundwater to meet the crop
ET requirements. The modelling results indicated that the
soil salinity risks would be high if the sugarcane crop
was grown for long periods over a saline shallow wa-
tertable (≤ 2 m). The best management strategy would be
to lower the watertable below 2 m depth by artificial
drainage first and then apply regular leaching applica-
tions to flush excessive salts into the drainage system.
7. Acknowledgements
This research was undertaken as a partnership between
the Department of Agriculture and Food, Western Aus-
tralian (DAFWA) and CSIRO Land and Water. Project
funding was provided by the Australian Government and
the Government of Western Australia through the Na-
tional Action Plan for Salinity and Water Quality project

Modelling Irrigation and Salinity Management Strategies in the Ord Irrigation Area55
033016: Improved Water Management in the Stage 1
Ord River Irrigation Area. In-kind contributions were
provided by CSIRO Water for a Healthy Country Flag-
ship and DAFWA. Information and support from grow-
ers has been critical for the field experiments and data
collection.
REFERENCES
[1] K. K. Tanji and N. C. Kielen, “Agricultural Drainage
Water Management in Arid and Semi-Arid Areas,” FAO
Irrigation and Drainage, FAO, Rome, 2000.
[2] P. Rengasamy, “World Salinisation with Emphasis on
Australia,” Journal of Experimental Botany, Vol. 57, No.
5, 2006, pp. 1017-1023.
[3] A. J. Smith, “Rainfall and Irrigation Controls on Ground-
water Rise and Salinity Risk in the Ord River Irrigation
Area, Northern Australia,” Hydrogeology Journal, Vol.
16, No. 6, 2008, pp. 1159-1175.
[4] A. J. Smith, D. Pollock and D. Palmer, “Review of
Groundwater Monitoring and Reporting in the Ord Stage
1 Irrigation Area,” Kununurrra, Western Australia, 2006.
[5] R. Salama, E. Bekele, L. Bates, D. Pollock and V. Gailitis,
“Hydrochemical and Isotopic Characteristics of the Sur-
face and Groundwater of the Hydrological Zones of the
Ord Stage I Irrigation Area,” CSIRO Land and Water
Technical Report 8/02, 2002.
[6] R. Ali, R. Salama, D. Pollock and L. Bates, “Geochemi-
cal Interactions Between Groundwater and Soil, Ground-
water Recycling and Evaporation in the ORIA,” CSIRO
Land and Water Technical Report 21/02, 2002.
[7] R. Ali, R. L. Elliott and J. E. Ayars, “Soil Salinity Model-
ling under Shallow Watertable Conditions: (ii) Model
calibration,” Journal of Irrigation Drainage Engineering,
Vol. 126, No. 4, 2000a, pp. 234-242.
[8] A. R. L. Elliott and J. E. Ayars, “Soil Salinity Modelling
under Shallow Watertable Conditions: (ii) Model Appli-
cation,” Journal Irrigation Drainage Engineering, Vol.
126, No. 4, 2000b, pp. 234-242.
[9] J. K. Ruprecht and S. J. Rodgers, “Hydrology of the Ord
river,” Water and Rivers Commission, Water Resources
Technical Series No. WRT 24, 1999
[10] T. C. Stoneman, “Packsaddle Plains Soil Survey,” West-
ern Australia Department of Agriculture, Technical Bulle-
tin No. 55, 1972.
[11] J. M. Aldrick, A. J. Clarke, P. W. Moody, M. H. R. van
Cuylenburg and B. A. Wren, “Soils of the Ivanhoe Plain,
East Kimberley, Western Australia,” Western Australian
Department of Agriculture Technical Bulletin 82, 1990.
[12] N. Schoknecht, “Assessment of the suitability for agri-
culture of the North-west Packsaddle Area, Kununurra,”
Department of Agriculture Western Australia, Resource
Management Technical Report 156, 1996.
[13] N. Schoknecht and C. Grose, “Soils of the Ivanhoe West
Bank East Kimberley Western Australia,” Department of
Agriculture Western Australia, Resource Management
Technical Report 1551, 1996.
[14] G. M. Plunkett and R. C. Muchow, “Water Extraction by
Sugarcane on Soils of the Ord Irrigation Area,” Austra-
lian Journal of Experimental Agriculture, Vol. 43, No. 5,
2003, pp. 487-495.
[15] A. L. Chapman, J. D. Sturtz, A. L. Cogle, W. S. Mollah
and J. R. Bateman, “Farming Systems in the Australia
Semi-Arid Tropics: A Recent History,” Australian Jour-
nal experimental Agriculture, Vol. 36, No. 8, 1996, pp. 915-
928.
[16] CSIRO, “Water in the Timor Sea Drainage Division, a
Report to the Australian Government from the CSIRO
Northern Australia Sustainable Yields Project,” CSIRO
Water for a Healthy Country Flagship, Australia, 2009, p.
508.
[17] C. W. Rose, P. W. Dayananda, D. R. Nielsen and J. M.
Biggar, “Long Term Solute Dynamics and Hydrology in
Irrigated Slowly Permeable Soils,” Irrigation Science,
Vol. 1, No. 2, 1979, pp. 77-87.
[18] Kinhill Pty. Ltd., “Water Infiltration Modelling for the
Ord Sugar Project,” Ord River Irrigation Area Stage 2
Proposed Development of the M2 Area, Consultancy Re-
port for Wesfarmers Ltd. Marubeni Corporation and the
Water Corporation of Western Australia, Perth, August
1999.
[19] J. L. Hutson and R. J. Wagenet, “LEACHM ‘Leaching
Estimation a Chemistry Model’ A Process-Based Model
of Water and Solute Movement, Transformations, Plant
Uptake, and Chemical Reactions in the Unsaturated
Zone,” 3rd Edition, Cornell University, Ithaca, New York,
1992.
[20] G. S. Campbell, “A Simple Method for Determining Un-
saturated Conductivity from Moisture Retention Data,”
Soil Science, Vol. 117, No. 6, 1974, pp. 311-314.
[21] J. L. Hutsonand and A. Cass, “A Retentivity Function for
Use in Soil-Water Simulation Models,” Journal of Soil
Science, Vol. 38, No. 1, 1987, pp. 105-113.
[22] B. J. Cosby, G. M. Hornberger, R. B. Clapp and T. R.
Ginn, “A Statistical Exploration of the Relationships of
Soil Moisture Characteristics to the Physical Properties of
Soils Stochastic Modelling,” Water Resources Research,
Vol. 26, No. 6, 1984, pp. 682-690.
[23] J. L. Hutson, “Water Retentivity of Some African Soils in
Relation to Particle Size Criteria and Bulk Density,”
South African Journal for Plant and Soil, Bureau for Sci-
entific Publications, Foundation for Education, Science
and Technology, Vol. 3, No. 4, 1986, pp.151-155.
[24] W. J. Rawls and D. L. Brakensiek, 1985. “Prediction of
Soil Water Properties for Hydrologic Modelling,” In: E.
B. Jones and T. J. Ward, Eds., Proceedings of a Sympo-
sium Watershed Management in the Eighties, New York,
30 April-1 May 1985, pp. 293-299.
[25] S. W. Childs and R. J. Hanks, “Model of Soil Salinity
Effects on Crop Growth,” Soil Science Society of America
Proceedings, Vol. 39, No. 4, 1975, pp. 617-622.
[26] S. W. Childs, “A Model to Predict the Effect of Salinity
on Crop Growth,” Utah State University, Logan, Utah,

Modelling Irrigation and Salinity Management Strategies in the Ord Irrigation Area 56
1975.
[27] J. M. Davidson, D.A. Graetz, C. Rao and H. M. Selim,
“Simulation of Nitrogen Movement, Transformation and
Uptake in Plant Root Zone,” EPA-600/3-78-029, 1978.
[28] M. N. Nimah and R. J. Hanks, “Model for Estimating Soil
Water, Plant, and Atmospheric Interrelations, I. Descrip-
tion and Sensitivity,” Soil Science Society of America
Proceedings, Vol. 37, No. 4, 1973, pp. 522-527.
[29] B. J. Bridge and R. C. Muchow, “Soil Water Relation-
ships for Cununurra Clay and Ord Sandy Loam in the Ord
River Irrigation Area,” Tropical Agronomy Technical
Memorandum Number 30, CSIRO division of Tropical
Crops and Pastures, St. Lucia, Brisbane, Queensland,
Australia, 1982.
[30] OGTR, “The Biology of Zea Mays L. Ssp Mays (Maize
or Corn),” Office of the Gene Technology Regulator
(OGTR), Australian Government, Department of Health
and Ageing, 2008, p. 80.
[31] M. Kondo, M. V. R. Murty, and D. V. Aragones, “Char-
acteristics of Root Growth and Water Uptake from Soil in
Upland Rice and Maize under Water Stress,” Soil Science
and Plant Nutrition, Vol. 46, No. 3, 2000, pp. 721-732.
[32] L. M. Dwyer, B. L. Ma, D. W. Stewart, H. N. Hayhoe, D.
Balchin, J. L. B. Culley and M. McGovern, “Root Mass
Distribution under Conventional and Conservation Till-
age,” Canadian Journal of Soil Science, Vol. 76, No. 1,
1996, pp. 23- 28.
[33] E. L. Anderson, “Tillage and N Fertilization Effects on
Maize Root Growth and Root: Shoot Ratio,” Plant Soil,
Vol. 108, No. 2, 1988, pp. 245-251.
[34] R. E. Danielson, 1967. “Root Systems in Relation to Irri-
gation,” In: R. M. Hagan, H. R. Haise and T. W. Edmin-
ster, Ed., Irrigation of Agricultural Lands, American So-
ciety of Agronomy, Wisconsin, 1967, pp. 390-413.
[35] C. J. Willmott, “On the Validation of Models,” Physical
Geography, Vol. 2, No. 2, 1981, pp. 184-194.
[36] T. Roberts, N. Lazarovich, A. W. Warrick and T. L.
Thompson, “Modelling Salt Accumulation with Subsur-
face Drip Irrigation Using HYDRUS-2D,” Soil Science
Society of America Journal, Vol. 73, No. 1, 2009, pp. 233-
240.
[37] R. C. Muchow, “Comparative Productivity of Maize,
Sorghum and Pearl Millet in a Semi-Arid Tropical Envi-
ronment. II. Effect of Water Deficits,” Field crops Re-
search, Vol. 20, No. 3, 1989, pp. 207-219.
[38] K. S. Fischer and F. E. Palmer, “Tropical Maize,” In: P. R.
Goldsworthy and N. M. Fischer, Eds., The Physiology of
Tropical Field Crops, Wiley, Chichestor, 1984, pp. 213-
248.
[39] M. J. Robertson, N. G. Inman-Bamber, R. C. Muchow
and A. W. Wood, “Physiology and Productivity of Sug-
arcane with Early and Mid-Season Water Deficit,” Filed
Crops Research, Vol. 64, No. 3, 1999, pp. 211-227.
[40] R. C. Muchow and B. A. Keating, “Assessing Irrigation
Requirements in the Ord Sugar Industry Using a Simula-
tion Modelling Approach,” Australian Journal of Ex-
perimental Agriculture, Vol. 38, No. 4, 1998, pp. 345-
354.
[41] L. K. Stewart, P. B. Charlesworth, K. L. Bristow and P. J.
Thorburn, “Estimating Deep Drainage and Nitrate Leach-
ing from the Root Zone Under Sugarcane Using AP-
SIM-SWIM,” Agricultural Water Management, Vol. 81,
No. 3, 2006, pp. 315-334.
[42] C. A. Hurst, P. J. Thorburn, D. Lockington and K. L.
Bristow, “Sugarcane Water Use from Shallow Water Ta-
bles: Implications for Improving Irrigation Water Use Ef-
ficiency,” Agricultural Water Management, Vol. 65, No.
2004, 2004, pp.1-19.
[43] G. Hunsigi and S. C. Srivastava, “Modulation of ET
(Evapotranspiration) Values of Sugar Cane Because of
High Water Table,” Proceedings of the 16th ISSCT Con-
gress, Australia, 1977, pp. 1557-1564.
[44] Y. S. Chauhan, “Potential Productivity and Water Re-
quirements of Maize-Peanut Rotations in Australian
Semi-Arid Tropical Environments: A Crop Simulation
Study,” Agricultural Water Management, Vol. 97, No. 3,
2010, pp. 457-464.