RSMG’s Murray-Darling Basin
Optimisation Model Documentation
Version: April 2010
RSMG
School of Economics
The University of Queensland
St Lucia
QLD 4078
Background to the Report
The RSMG Murray-Darling Basin Optimisation Model is an integrated economic-hydrological
model of resource use, farm production and externalities in the Murray Darling Basin
(hereafter Basin). The model incorporates risk and uncertainty in water use decisions using a
state-contingent approach, where allocation decisions reflects water availability under
different states of nature and the available technological options to utilise water under
different states of nature, such as drought, wet or normal years in relation to available
water. This treatment of uncertainty enables the explicit representation of variability of
both inputs such as water, capital and management, and the outputs in agricultural,
environmental and urban uses as influenced by the states of nature. In this way the model
can produce estimates of the expected benefits at a catchment and the whole of Basin level
from a range of economic activities relating to water use. This is represented by a set of key
commodities allocated optimally in meeting with a set of water availability and land use
constraints.
Key capabilities of the model include:
 Generation of baseline scenarios for land use, production value, and instream
salinity consistent with historical experience;
 Scenario analysis for alternative assumptions on climate change, resource availability
and policy objective settings;
 Demonstration of adaptability and resilience of agricultural enterprises in different
regions by way of production systems that vary by state of nature; and
 Identification of potential sources of costs and benefits and their trade-offs for
alternative resource use scenarios.
The model was initially developed in 2004 by the Risk and Sustainable Management Group
(hereafter RSMG) at the University of Queensland, funded the Australian Research Council
(ARC) under ARC Federation Fellowships awarded to Professor John Quiggin (2003 and
2007). The conceptual basis of the modelling framework and its application has been
subjected to international peer review. See for example Adamson, Mallawaarachchi and
Quiggin (2007, 2009).
This documentation aims to detail how the model is implemented and the data sources
used. The model is constantly being updated and more recent versions may differ in both
functionality and spatial disaggregation from the documentation provided in this document.
Please consult the RSMG web site (http://www.uq.edu.au/rsmg/) for developments.
This report may be cited as:
RSMG, 2010, RSMG’s Murray-Darling Model Documentation: Version April 2010, Risk and
Sustainable Management Group, The University of Queensland, Brisbane.
© RSMG, School of Economics, University of Queensland 2010
This work is copyright. The Copyright Act 1968 permits fair dealing for study, research, news
reporting, criticism or review. Selected passages, tables or diagrams may be reproduced for such
purposes provided acknowledgment of the source is included. Major extracts or the entire
document may not be reproduced by any process without written permission from Professor John
Quiggin.
Risk and Sustainable Management Group
School of Economics,
University of Queensland
St Lucia
4072
Telephone:
+61 7 3346 9646
Facsimile:
+61 7 3365 7299
Internet:
http://www.uq.edu.au/rsmg/
Blog: http://www.johnquiggin.com/rsmg/wordpress/
Acronyms
Acronym
ABARE
ABS
ACT
ARC
BSMS
EC
GL
GMB
H
Ha
L
MDBC
ML
NSW
QLD
RSMG
SA
SAMDB
SDL
SLA
VIC
Description
Australian Bureau of Agricultural and Resource Economics
Australian Bureau of Statistics
Australian Capital Territory
Australian Research Council
Basin Salinity Management Strategy
Electrical conductivity is a measure of salinity concentration in water
Giga-litre, is 1,000 ML of water
Gross Margin Budgets
High water use irrigation technology (e.g. flood irrigation)
Hectare
Low water use irrigation technology (e.g. drip irrigation)
Murray Darling Basin Commission
Mega-litre = 1 million litres of water
New South Wales
Queensland
Risk and Sustainable Management Group
South Australia
South Australian Murray Darling Basin a catchment within the Basin
Sustainable Diversion Limit
Statistical Local Area
Victoria
Glossary
Term
Basin
Basin Salinity Management Strategy
Cap
Conjunctive water use
Conveyance loss
Economic efficiency
Economic value
Environmental assets
Environmental flow
Gross Margin Budgets
Gross Output
Groundwater
Inflows
Description
The Murray Darling Basin, that encompass
parts of NSW, Queensland, South Australia,
and Victoria, and the Australian Capital
Territory.
Establishes targets for the river salinity of
each tributary valley and the Murray-Darling
system
An upper limit on the volume of water
extracted for consumptive use from a
waterway, catchment, basin, or aquifer
Conjunctive water use refers to
simultaneous use of surface water and
groundwater to meet crop demand.
Water evaporation and seepage from
surface water sources and man-made water
transportation facilities
An activity is economically efficient if the
resources used in that activity cannot be
reallocated to produce a greater output.
That means no one can be made better off
without making someone worse off; and the
output is produced at the least cost for all
combinations of inputs and outputs.
Net returns from a production system having
accounted for capital and variable costs
including operator labour.
This includes water-dependent ecosystems,
ecosystem services and sites with ecological
significance
A water regime provided within a river,
wetland or estuary to improve or maintain
ecosystems, where there are competing
water uses and where flows are regulated
Provide information about an activity
employing a set of technologies and their
related yields, variable production costs
(water, labour, chemicals, and machinery)
and returns based on indicative prices.
Returns from a production system
considering yield and price only.
Water that is sourced from below the earth’s
surface.
The surface water that reaches the river
Inter-Basin Water Transfers
Murray Darling Basin Cap
Reflow
States of nature
Statistical Local Area
Total Returns
generated from seasonal runoff from
rainfall, specific to a catchment or area.
Water that is transferred into the Basin from
another water source, such as the transfers
from the Snowy River to the MurrayMurrumbidgee system.
The water extraction limits established by
the MDBC for water that can be diverted
from the rivers for consumptive use.
The amount of water that returns to the
stream network once it has been utilised for
irrigation purposes.
A mutually exclusive set of possible
descriptions of the states of the world (e.g.,
drought, normal and wet states of runoff)
A spatial unit used to collect and disseminate
statistical information.
Return from a production system not
including adjustments for operator wages
and labour costs.
Introduction
The RSMG Murray-Darling Basin Model optimises water, land, labour and capital use to
maximise economic returns from irrigation under uncertainty. The model follows a directed
flow network of water resources based on the hydrological structure of the Basin. The
model is designed to simulate the benefits of water use at a catchment level, by accounting
for the tradeoffs between different water users. The relationship between irrigated
production and instream salinity in the Basin is also represented in the model. The model
described here is derived from an updated version on the state contingent Murray-Darling
Basin Model documented in Adamson, Mallawaarachchi and Quiggin (2007 and2009).
The model has been developed in two platforms:
 General Algebraic Modelling System (GAMS) (http://www.gams.com/)
 Microsoft Excel using Risk Solver Platform (http://www.solver.com/).
This approach has provided a quality assurance framework to ensure that the models
interpret, collate and process the data in a consistent manner.
There are two principal ways of optimising the model:
 Sequential – where the model optimises water use for each catchment sequentially
in order of the hydrological flow structure. This does not allow for trade between
catchments, and upstream resource use and activities are made without regard for
downstream opportunity costs.
 Global – where the model optimises water use for the Basin as a whole. Here the
global optimum maximises returns for the entire Basin taking account of upstreamdownstream interactions in water use and salinity.
Earlier versions of the model have been used to undertake a wide range of analysis.
Previously commissioned outputs from the model are outlined in Error! Reference source
not found.. These reports are not available at the RSMG web site apart from the Garnaut
Contribution (http://www.uq.edu.au/rsmg/)
This report has been divided into the following segments:
 An introduction into the state-contingent approach to risk and uncertainty
 Model assumptions and data used;
Rationale behind State Contingent Approach to Risk and Uncertainty
State Contingent Approach to Risk & Uncertainty
In most models risk and uncertainty is simply dealt with in a stochastic framework. That has
the tendency to discount the extremities of climate variability on production choice and
thereby assumes that producers do not respond to changes in state of nature by altering the
inputs to produce alternative sets of outputs that are feasible and profitable within their
means. If we consider for example, that wheat produced in a wet state of nature is the
same as wheat produced in a drought we have effectively ignored the influence of variety,
screenings, protein and moisture levels which influence the price received.
Recent developments in state contingent analysis is due to Chambers and Quiggin
(Chambers & Quiggin 2000) who re-examined the foundations described by Arrow (1953)
and Debreu (1959). It suggests that decision makers actively respond to states of nature, by
altering the inputs to influence the final output, based on past experiences and knowledge
in order to meet their objective function. The benefits of a state contingent approach is
that it allows for production and decision maker uncertainty to be treated separately
Rasmussen (2006). This division removes the blurring of ambiguity found in other decision
support systems where production and management inefficiency cannot be separated
O'Donnell & Griffiths (2006).
Suggested Further Reading
Chambers, R. G., Quiggin, J., 2000, Uncertainty, Production, Choice and Agency: The State –
Contingent Approach, Cambridge University Press, New York
Rasmussen, S 2003, 'Criteria for optimal production under uncertainty. The state-contingent
approach', Australian Journal of Agricultural and Resource Economics, vol. 47, no. 4, pp.
447-76.
O'Donnell, CJ & Griffiths, WE 2006, 'Estimating State-Contingent Production Frontiers', The American
Journal of Agricultural Economics, vol. 88, no. 1, pp. 249-66.
Model Design
Objective Function = Maximise the weighted average economic return from irrigation
use across the three states of nature
Constraints
Global model
 Water availability
 Adelaide salinity target
 Water use caps (Regional)


Operator labour
Irrigation area
Sequential model
 Water availability
 End of Valley salinity targets
 Water use caps (Catchment)


Operator labour
Irrigation area
Notes:lll
 Economic Return =( Gross Return – Operator Labour – Capital Costs ) + Water Use
Value
Assumptions
Data
 Gross Return =and
(Yield
* Price) – Variable costs (including casual labour)
 Conjunctive Water Supply= Runoff+ Ground Water + Inter-Basin Water Transfers
States of nature
The model uses the state contingent approach to reduce the uncertainty surrounding water
availability. This is done by explicitly representing inflow variability as states of nature. In
this model three states of nature are used to represent normal, drought and wet years. The
states of nature are defined by their probability of occurrence. For the purpose of this
model it has been assumed that the probability of a normal year is 0.5, a drought year is 0.2,
and a wet year is 0.3 (Table 1). These assumptions are based on historical records confirmed
through personal correspondence with the Murray Darling Basin Commission in July 2007.
State contingent production is explicitly modelled. This allows a model to illustrate how
producers effectively use their inputs to maximise the return on their asset base taking into
account the highly variable nature of water availability. The model stipulates that producers
are highly adaptive and responsive to climatic events and will alter their inputs to maximise
their overall net return on resources.
Water inflows, salt loads, and the inputs and outputs of production system vary by state of
nature (Table 1).
Time frame
The model has an annual time frame, but represents a medium term outlook. This means
that the model optimises the weighted average of the net economic returns associated with
each state of nature.
Spatial representation
The model represents 19 Catchment Management Regions (CMR) as well as Adelaide and
Flows to the Sea. These CMR’s are adjusted from Natural Resource Management Regions
(NRM) to improve the structure of modelled flows through the Basin. The 16 NRM regions
within the Basin has been disaggregated to 19 CMRs. NRM regions are based on catchments
bioregions, as well as state boundaries, and were established in agreements between
Commonwealth, state and territory governments in 2004 (Australian Government Land and
Coasts 2010).
The modifications from NRM regions include: Queensland Border Rivers and Maranoa
Balonne represented seperately based on river basin boundaries; Western and SAMDB
adjusted to fit within the borders of the Basin; ACT assimilated into the Murrumbidgee
catchment; and the Murray catchment disaggregated into three parts, based on SLA
boundaries, to facilitate the modelling of water sharing between NSW and Victoria.
Adelaide is modeled to account for water quality. The Flows to Sea provides a proxy for
environmental flows reaching the Coorong.
Flow Structure
The Basin is represented as a directed network of flow. It is simplistic in hydrology terms
due to the large spatial scale. The flow structure links the CMR units based on the
hydrological connection of rivers and the movement of surface water, illustrated in Figure 1.
Northern
Territory
Western
Australia
South
Australia
Queensland
New South
Wales
Victoria
Adelaide
Flows to Sea
Figure 1 Direction of flows
Catchment and Total flows
In each CMR the catchment flow of water is calculated from runoff flows, groundwater and
inter-basin transfers.
Catchment flowk…k21 = runoffk + groundwaterk + transfersk
Estimations of average runoff for each CMR are adapted from MDBC (2003) and ABS (2008).
Runoff is state contingent to represent variable nature of water availability. For each state
of nature a proportion of average runoff is assigned based on variability over the entire
Basin (MDBC 2009). The runoff of a ‘Normal’ state is 100 percent of average runoff, the
‘Drought’ state is 60 percent, and the ‘Wet’ is 120 percent.
While runoff is state contingent, the quantity of groundwater and transfers in each
catchment is assumed to be constant. In the model groundwater sources contribute a total
of 1,228 GL of water to the system. Inter-Basin transfers from the Snowy River to the
Murrumbidgee, Murray 1 and North East catchments provide a total of 1,118 GL of water to
the system. This data has been adapted from MDBC (2003) and ABS (2008). Catchment
inflows for a normal year, groundwater, and inter-basin are shown in Table 2.
Conveyance loss
Conveyance loss is represented as a percentage loss to the water flowing through the basin,
based on data from MDBC (2003, 2006) water resource fact sheets. The percentage
conveyance loss is catchment specific, and it is considered to be constant over all states of
nature (Table 2).
Net flow in a CMR is subject to the conveyance loss in the system. The net flow of a
catchment is the maximum water that is available for use. Water use is subtracted from the
net water use, and the remaining is the residual flow.
Net flowks = Catchment flowks *(1 - Conveyance lossks %)
+ (Σ Upstream Residual Flow(k…)s )*(1 - Conveyance lossks %)
Net flow for downstream catchments is the sum of catchment flow and the residual flow of
any upstream catchments, based on the flow structure in Table 2. Residual flow from
upstream is also subject to the conveyance loss of each catchment it passes through.
Water Use
Water use is determined through the optimisation of land use to maximise economic
returns.
This model assumes that irrigation water cannot be fully contained on farm and that a
proportion of the water makes its way back to the water supply as reflow. The proportion of
reflow from irrigation is state contingent, 30 percent in a ‘Normal’ year, 10 percent in a
‘Drought’ and 40 percent in a ‘Wet’, and is assumed to be constant across the Basin. This
assumption simulates the practice of overwatering to drive salts away from the root zones
that accumulate in drought times. Net water use takes into account reflow from irrigation.
Net water use = Water use * (1- Reflowks %)
Once net water use is calculated for a catchment it is subtracted from the net flow.
Residual flowk = Net flowks - Net water use
Constraint
Water use is subject to physical and administrative constraints.
Firstly, water use in each catchment is constrained by the availability of water, where water
use cannot exceed the net flow.
Water Use < Net Flowks
Secondly water use is constrained by a cap in each CMR for irrigation and urban water use.
The surface water cap is based on the MDBC Cap, and data is adapted from MDBC Water
Use reports (various MDBC publications listed in References). The cap on water for irrigation
is 13,231 GL for the Basin and includes both surface and ground water. It is assumed that
the cap on groundwater is equal to the quantity of groundwater available. The cap ‘other’
(or urban water) is 403 GL, and is reflective of the population in each CMR. This is based on
MDBC 2006, Water resource fact sheet.
Water Use < Capks
Water quality
The model uses salinity as a proxy for water quality. The salinity level is determined by
natural salt loads and mobilization, stream flow, salt caused by reflow and mitigation
activities.
Natural salt load data is based on MDBC Salinity Reports. The natural salt load is state
contingent. It is assumed that 60 percent of the ‘Normal’ salt load is mobilised in the
‘Drought’ state and 130 percent is again mobilised in the ‘Wet’ state (Table 1). This
represents natural conditions where low rainfall periods do not mobilise the salt in the soil
profile. The natural salt load data for a normal state is shown in Table 2.
Salinity level depends on the stream flow. A higher stream flow reduces salinity as a given
amount of salt is diluted within a greater volume of water. The salinity is measured in
electronic connectivity (EC).
Catchment Salinity in EC= (Cumulative salt load (T)/ Cumulative flow (ML)) / 0.64
We assume that the reflow mobilises salt load that are captured in irrigated soils. The
parameter that controls the amount of salt carried by the reflow is Theta. It assumes that
salt mobilisation from reflow is 0.04 in Normal and Wet states, and lower (0.03) in Drought
states. The theta value is applied to the upstream water use. Subsequently, the salt carried
by reflow is state contingent. Theta is state contingent (Table 1).
Salt from reflow = Reflow * Theta
Salt mitigation schemes are assumed to extract 480,000 tonnes per annum of salt from the
system. The location and quantity of salt removed is shown in Table 2, based on data MDBC
2007.
Constraint
Activities in the sequential model are constrained by catchment specific ‘End of Valley
Targets’ and safe drinking water recommendations for Adelaide (MDBC 2001, 2005). These
targets reflect the maximum salt load allowed to be occurring at the end of a catchment
before the flow enters the next. Table 2 documents the catchment salinity targets as
assumed in the model.
The water quality in the global model is only constrained by Adelaide’s End of Valley Target
of 800 EC.
Previous versions of the model incorporated crop salinity thresholds and damage slopes of
crop yields as constrains to salinity carried in the system. We are currently investigating the
use of both approaches, salinity thresholds for crops and ‘End of Valley Targets’ as
constraints to salinity in the model.
Land Area
Agricultural land and water use in each region is modelled by a representative farmer with
agricultural land area Lk. Average farm size data for each commodity in each of the
catchments were adjusted from ABS 2001.
Table 3 shows the data assumptions used for farm sizes within the CMRs.
The model uses irrigation production area data derived from the 2000-01 production
season, as it is considered the last ‘normal’ year and this data was derived from ABS (2002)
data at the SLA level. This data is aggregated to CMR level using Geographic Information
System (GIS) software.
In order to allow growth and development in all regions the model allows the total irrigated
area to be expanded by up to 70 per cent, and specifically for high value activities such as
horticulture by up to 45 per cent.
Constraints
The area dedicated to horticulture in any catchment must be less than equal to the
horticultural constraint in that area.
The total area dedicated to irrigation in any region must be less than the total area available
in that region.
The operator labour to undertake the irrigation activity mix in a region, is less than the total
amount of labour available.
Production Systems
23 Production systems are included in the model based on land use practices. The rationale
for the use of production systems is to reflect the adaptability of agricultural producers to
alternate land use decisions subject to the availability of water in the states of nature. By
using production systems we are able to incorporate crop rotations as well as monocropping of some commodities. Production systems are state contingent, and commodities,
inputs, and outputs may vary depending on the state of nature. Table 4 outlines the 23
production systems and their state contingent commodities.
Flexible production systems change commodities grown by state of nature. For example
Wheat/Rice produces dryland wheat in the normal and dry state, and rice in the wet state.
Perennial production systems such as dairy and horticultural crops do not change
commodities, but incur higher costs or yield losses to maintain their crop based on input
availability.
Some horticultural crops are classified as either “high” or “low” technology. “High” employs
highly efficient irrigation technology in the production process, and “low” employs less
efficient irrigation technology. The use of high technology is usually associated with higher
cost of inputs.
Crop rotations can affect the yields of the crops. It is assumed that in the Wheat/Legume
production system that the wheat yield benefits from the rotation compared to monocropped wheat by 10% in normal years.
Gross margin budget data
Gross margin budgets provide about input and output costs and quantities associated with
farming activities. Gross margins refer to the total income, less the variable costs incurred in
the enterprise. Overhead cost, such as rates, insurance, administration, and permanent
labor are excluded from gross margins.
Production costs and yields are based on data from 398 GMBs. The data from the GMBs is
outlined in Table 5. Where possible, commodity and production system data is obtained
from each CMR, however when this is not possible the closest match is applied Table 6. The
data was adjusted for inflation, all costs of production and commodity prices are in2008
values.
In the model we considered the commodities sheep and beef based on GMB data available
from the NSW Department of Primary Industries (DPI) (2008b). The Dry Sheep Equivalent
(DSE) was used to compare sheep and beef enterprises and carrying capacities for pasture
types. Separate from sheep and beef production we included irrigated pasture as a feeding
method. Variable costs for fertilizer, herbicide and irrigation as well at machinery hours
were taken from Spray Irrigated Lucerne GMB (NSW DPI 1999). We assumed that using
irrigated pasture feed will make other fodder feeding redundant. The optimum stocking
rates vary with climate, enterprise, management and risk. We use regional stocking rates for
irrigated pasture recommended as by the NSW DPI (2008.a).
Production inputs and costs
Variable costs per hectare are compiled from GMB data sets. Included are hired labour
costs, chemical costs, contractor costs, machinery costs, and other variable costs.
The hired labour costs are a product of the labour hours and the casual labour rate, fixed at
$15.5 based on …source.
The total variable costs from the GMBs are assumed to be for a ‘Normal’ year. Adjustment
cost per hectare represent the costs associated with adjusting to a ‘Drought’ and ‘Wet’ state
of nature.
Variable costs = Chemical costs + Contractor costs + Machinery costs + Labour costs +
State contingent adjustment cost
Fixed costs per hectare include water costs, operator labour costs, and the annualised
repayment rate for capital divided by the average production system farm size for each
catchment.
The water cost is the product of the water requirement and the water price. The water
requirement for each commodity is state contingent. The water multiplier (Table 7) for each
state of nature is applied to the water per hectare needed for each commodity (based on
GMBs). Water price is a constant, set to $25/ML, and has no impact on commodity
selection. This assumption is based on…
The operator labour cost is the product of machinery hours required per hectare by state of
nature (Table 7) and the operator labour rate, which is set at $25 per hour. This assumption
is based on…
The model allows the use of capital costs to be included in the analysis. Because the model
is solved on an annual basis, the process of capital investment is modelled as an annuity
representing the amortised value of the capital costs over the lifespan of the development
activity. The This provides the flexibility to permit the modelling of a range of pricing rules
for capital, and to allow the imposition of appropriate constraints on adjustment, to derive
both short run and long run solutions. The farm establishment costs and costs of required
equipment and their recovery periods were obtained by production system, from a range of
sources. The adapted capital costs as used in the model are shown in Table 7.
Fixed costs = water cost + operator labour cost + annualised capital payments
The data is adjusted for inflation and all costs of production and commodity prices are in
2008 values. The interest rate in this model was assumed to be 7 percent per annum, and
capital costs are settled annually using a fixed repayment structure.
Total costs = Variable costs + Fixed costs
Yield and commodity price
The state contingent yield for each commodity is the product of the GMB yield and the yield
multiplier presented in Table 7. For a single commodity production system the yield is taken
directly from the GMB yield data for each catchment. For multiple commodity production
systems the yield is a weighted average between the commodities, based on the proportion
of production. For example in a normal year the Sheep-Wheat production system has a
proportion of 50% sheep and 50% wheat. Multiple commodity yield per hectare is
calculated as:
Yieldmulti = (Yield1*Prop1 + Yield2*Prop2)* YieldMultiplier
The prices of commodities are based on the commodity prices shown in Table 8. For single
commodity production systems the price is taken directly from the commodity price sheet
for all catchments. For multiple commodity production systems the price depends on the
proportional combined yield of the system. Multiple commodity price per hectare is
calculated as:
Pricemulti = (Yield1*Price1*Prop1 + Yield2*Price2*Prop2)/ Yieldmulti
Return per hectare
The return per hectare is state contingent, based on the commodity, yield, price and costs
as calculated earlier. The return can be defined as:
Return per hectare = Yield * Price - Costs
The return per hectare for production systems by catchment for each state of nature is
shown in Table 9, Table 10 & Table 11.
Table 1 Summary of Basin wide state contingent assumptions
State probability of
occurrence
Inflow
Reflow
Salt mobilisation
Theta
Normal
Drought
Wet
0.5
0.2
0.3
1
0.3
1
0.04
0.6
0.1
0.6
0.03
1.2
0.4
1.3
0.04
Table 2 Summary Catchment Assumptions
Catchment
Flow Structure
Runoff
(GL)
Ground
Water
(GL)
Water
Transfer
(GL)
Total
Water
(GL)
Conveyance
Loss
Cap
Other
(GL)
Surface
Cap
(GL)
Total
Irrigation
Cap (GL)
Salt
Raw (T)
Condamine
Border Rivers QLD
Warrego Paroo
Namoi
Central West
Maranoa Balonne
Border Rivers Gwydir
+Condamine
+ QBRivers
+Warrego-Paroo
+Namoi
+Central West
+Maranoa-Balonne +Condamine(Res)
+BRGwydir +QBRivers(Res)
+Western +Warrego-Paroo(Res) +Namoi(Res) +Central
West(Res) +Maranoa-Balonne(Res) +BRGwydir(Res)
+Lachlan
+Murrumbidgee
+North East
+Murray 1
+Goulburn Broken + ½(North East(Res) +Murray
1(Res))
+Murray 2 + ½(North East(Res) +Murray 1(Res))
+ North Central + ½(Goulburn Broken(Res) +Murray
2(Res))
+ Murray 3 + ½(Goulburn Broken(Res)+Murray 2(Res))
+Mallee + ½(North Central(Res) +Murray 3(Res) +
Lachlan(Res) +Murrumbidgee(Res))
+LMDB + ½(North Central(Res) +Murray 3(Res) +
Lachlan(Res) +Murrumbidgee(Res) +Western(Res)
+SAMDB +LMDB(Res) + Mallee(Res)
+ SAMDB(Res)
+ SAMDB(Res) – Adelaide Extractions
735
726
407
833
1,656
1,260
1,496
68
9
12
243
92
68
156
0
0
0
0
0
0
0
803
735
419
1,076
1,748
1,328
1,652
0.4
0.5
0.8
0.3
0.6
0.4
0.1
5
5
4
10
21
0
1
240
200
35
325
577
200
660
308
209
47
568
669
268
816
7,035
7,818
1,672
67,452
33,647
7,035
7,891
0
0
0
0
0
0
0
0
0
0
0
0.5
44
577
577
0
0
1,054
4,184
2,000
1,500
132
224
28
0
0
550
284
284
1,186
4,958
2,312
1,784
0.3
0.3
0.1
0.1
10
50
72
55
453
2,311
92
70
585
2,535
120
70
115,819
160,000
91,065
20,000
0
0
0
0
4,597
49
0
4,646
0.1
45
2,000
2,049
120,000
0
500
30
0
530
0.1
0
910
940
910
0
2,762
16
0
2,778
0.3
40
1,627
1,643
50,000
25,952
113
49
0
162
0.1
0
670
719
670
0
0
13
0
13
0.0
0
200
213
20,431
36,954
106
9
0
115
0.0
47
126
135
20,091
188,20
132
0
0
24,061
30
0
0
1,228
0
0
0
1,118
162
0
0
26,407
0.1
0.0
0.0
0
0
0
409
524
206
0
12,003
554
206
0
13,231
57,909
229,54
0
0
480,65
Western
Lachlan
Murrumbidgee
North East
Murray 1
Goulburn Broken
Murray 2
North Central
Murray 3
Mallee
Lower Murray Darling
SA MDB
Adelaide
Coorong
TOTAL
789,445
Salt
Mitigatio
(T)
Table 3 Catchment Production System Average Farm Size
CMAs
Condamine
Border Rivers
QLD
Warrego
Paroo
Namoi
Central West
Maranoa
Balonne
Border Rivers
Gwydir
Western
Lachlan
Murrumbidgee
North East
Murray 1
Goulburn
Broken
Murray 2
North Central
Murray 3
Mallee
Lower Murray
Darling
SA MDB
CitrusH
CitrusL
Grapes
Stone
FruitH
Stone
FruitL
Pome
Fruit
Vegetables
Cotton
Rice
Wheat
Dairy
-H
Dairy
-L
Sheep/
Wheat
Beef
Sheep
40
40
45
40
40
40
40
3,000
400
500
277
278
600
600
600
40
40
45
40
40
40
40
3,000
0
500
277
278
600
600
600
0
40
40
0
40
40
45
45
45
0
40
40
0
40
40
0
40
40
40
40
40
3,000
3,000
3,000
0
400
400
500
500
500
277
277
324
278
278
325
600
600
600
600
600
600
600
600
600
40
40
45
40
40
40
40
3,000
0
500
277
278
600
600
600
40
40
40
20
20
20
40
40
40
20
20
20
45
45
45
45
45
45
40
40
40
20
20
20
40
40
40
20
20
20
40
40
40
20
20
20
40
40
40
20
20
20
3,000
3,000
3,000
500
0
500
0
0
400
400
400
400
500
500
500
500
300
500
277
277
324
342
173
215
278
278
325
342
173
215
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
20
20
20
20
30
20
20
20
20
30
45
45
45
45
45
20
20
20
20
30
20
20
20
20
30
30
20
20
20
20
20
20
20
20
20
0
500
0
500
0
400
400
400
400
400
300
500
300
500
300
215
215
215
215
215
215
215
215
215
215
600
600
600
600
600
600
600
600
600
600
600
600
600
600
600
20
20
20
20
45
45
20
20
30
20
20
20
20
20
300
300
0
0
300
300
215
400
215
400
600
600
600
600
600
600
Table 4 Production system commodity by state of nature
Production System
1
2
3
4
5
6
7
8
9
10
11
12
Citrus-H
Citrus-L
Grapes
Stone Fruit-H
Stone Fruit-L
Pome Fruit
Vegetables
Cotton Flex
Cotton Fixed
Cotton/Chickpea
Dryland Cotton
Rice PSN
13
Dryland Wheat
14
15
Wheat
Wheat Legume
16
17
18
Sorghum
Oilseeds
Sheep Wheat
19
20
21
22
23
Dairy-H
Dairy-L
Beef
Sheep
Dryland
Commodity
Normal
Citrus
Citrus
Grapes
Stone Fruit
Stone Fruit
Pome Fruit
Vegetables
Cotton
Cotton
Cotton
Cotton
Rice 33%
Wheat 67%
Veg 10%
Wheat
Commodity
Dry
Citrus
Citrus
Grapes
Stone Fruit
Stone Fruit
Pome Fruit
Vegetables
Cotton
Cotton
Chickpea
Cotton
Rice 33%
Wheat 67%
Wheat
Wheat 50%
Legume 50%
Sorghum
Oilseeds
Fat Lamb 50%
Wheat 50%
Dairy
Dairy
Beef
Fat Lambs
Dryland
Wheat
Wheat 110%
Wheat
Sorghum
Oilseeds
Fat Lamb 90%
Wheat 10%
Dairy
Dairy
Beef
Fat Lambs
Dryland
Commodity
Wet
Citrus
Citrus
Grapes
Stone Fruit
Stone Fruit
Pome Fruit
Vegetables
Cotton
Cotton
Cotton
Cotton
Rice 33%
Wheat 67%
Veg 15%
Rice 33%
Wheat 67%
Veg 15%
Wheat
Wheat 40%
Legume 70%
Sorghum
Oilseeds
Fat Lamb 30%
Wheat 70%
Dairy
Dairy
Beef
Fat Lambs
Dryland
Table 5 GMB Data
Column
Yield
Price
Labour
Lab. Chg.
Tractor Hr
Water Req
Water Price
Chemicals
Contractor
Machinery
OVC
VC Excl.
Water
Description
Average yield per hectare of the commodity in the
respective CRM
Average real price of the commodity in the respective CRM
Average number of work hours per hectare for hired labour
Average real hired labour costs per hour
Average number of machinery hours per hectare
Average water volume (in ML) required per hectare
Price per ML – currently ignored
Average real costs per hectare of total chemicals required
Average real costs per hectare for contractors
Average real costs of machinery per hectare
Average real other variable costs per hectare
Total variable costs per hectare excluding water costs
Table 6 GMA data availability by Catchment
CMAs
CitrusH
Condamine
CitrusL
Grapes
1998
1998
Border Rivers
QLD
Warrego
Paroo
Namoi
Central West
Maranoa
Balonne
Border Rivers
Gwydir
Western
Lachlan
Murrumbidgee
Stone
FruitH
Stone
FruitL
Pome
Fruit
Vegetables
Cotton
Irr
Cotton
Dry
2000
2002
2003
2000
1999
2007
2004
Wheat
Legumes
Sorghum
Oilseeds
Other
Grains
2003
2003
2002
2002
2002
2002-03
2002
2002
200203
2002
2008
1999
2003
1999
1999
2003
2001
2003
2001
2003
2003
2007
Goulburn
Broken
Murray 2
North Central
2006
2006
2006
1999
1999
1999
Dairy
-H
Dairy
-L
Beef
2004
2004
2007
2004
2003
2008
2004
2004
2007
2004
2003
2008
2002
2002
2002
North East
Murray 1
Murray 3
Mallee
Lower Murray
Darling
SA MDB
Wheat
Dry
2002
200307
2003
Rice
2002-03
2004
2002
2004
2003
2003
2003
2004
2003
2003
2003
2007
200107
200307
2002-07
200307
2003-07
2003
2003
2008
2008
S
2
2
2
2
200307
2006
2
2008
200608
200608
2006-08
2006-08
2006-08
200608
2
2
2
2003
2008
2003
2008
2008
2008
2008
2
Table 7 Production System Price and Multipliers by State of Nature
Commodity A
Price
Citrus-H
Citrus-L
Grapes
Stone Fruit-H
Stone Fruit-L
Pome Fruit
Vegetables
Cotton Flex
Cotton Fixed
Cotton/Chickpea
Dryland Cotton
Rice PSN
Dryland Wheat
Wheat
Wheat Legume
Sorghum
Oilseeds
Sheep Wheat
Dairy-H
Dairy-L
Beef
Sheep
Dryland
$600
$600
$950
$1,600
$1,600
$1,300
$720
$620
$620
$620
$620
$295
$210
$210
$280
$185
$380
$92
$0.35
$0.35
$60
$45
$50
Normal
All
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
Price
Yield
$600
$600
$950
$1,600
$1,600
$1,300
$720
$620
$620
$300
$620
$223
$210
$210
$231
$185
$380
$52
$0
$0
$60
$45
$0
0.8
0.9
0.9
0.8
0.9
0.9
1.0
1.0
1.0
1.0
0.8
1.0
0.7
0.8
1.0
0.8
0.8
1.0
0.9
0.8
0.7
0.7
1.0
Dry
Water
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
Adjustment
Cost $
$20
$0
$20
$20
$0
$20
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$0
$50
$300
$300
$20
$20
1.0
Price
Yield
$600
$600
$950
$1,600
$1,600
$1,300
$720
$620
$620
$620
$620
$331
$331
$210
$319
$185
$380
$125
$0
$0
$60
$45
$65
1.2
1.2
1.2
1.2
1.2
1.2
1.0
1.0
1.0
1.0
0.9
1.0
0.9
1.1
1.0
1.1
1.1
1.0
1.5
1.2
1.2
1.2
1.0
Wet
Water
1.2
1.2
1.2
1.2
1.2
1.2
1.0
1.0
1.0
1.0
1.2
1.2
1.2
1.2
1.0
1.2
1.0
1.0
1.2
1.2
1.0
1.0
1.0
Adjustment
Cost $
$20
$100
$20
$20
$100
$20
$0
$100
$0
$100
$100
$100
$100
$50
$0
$100
$0
$0
$0
$0
$10
$15
1.0
Table 8 Commodity Price Data raw
Commodities
Citrus
Grapes
Stone Fruit
Pome Fruit
Vegetables
Cotton
Rice
Wheat
Grain Legume
Dairy
Fat Lambs
Beef
Sorghum
Oilseeds
Chickpea
Timber
Carbon
Adelaide
Dryland
Normal
Dry
$600
$950
$1,600
$1,300
$720
$620
$250
$210
$350
$0.35
$45
$60
$185
$380
$300
$31
$25
$500
$50
Wet
$600
$950
$1,600
$1,300
$720
$620
$250
$210
$350
$0.35
$45
$60
$185
$380
$300
$31
$25
$1,500
$0
Unit
$600
$950
$1,600
$1,300
$720
$620
$250
$210
$350
$0.35
$45
$60
$185
$380
$300
$31
$25
$500
$65
T
T
T
T
T
Bale + seed
T
T
T
Litre
$/DSE
$/DSE
T
T
T
M3
T
ML
HA
Table 9 Return per hectare Normal
CMAs
Condamine
Border Rivers
QLD
Warrego
Paroo
Namoi
Central West
Maranoa
Balonne
Border Rivers
Gwydir
Western
Lachlan
Murrumbidgee
North East
Murray 1
Goulburn
Broken
Murray 2
North Central
Murray 3
Mallee
Lower Murray
Darling
SA MDB
CitrusH
CitrusL
Grapes
Stone
FruitH
Stone
Fruit-L
Pome Fruit
Cotton
Flex
Veg
Cotton
Fixed
Cotton/
Chickpea
Dryland
Cotton
Rice
PSN
Dryland
Wheat
Wheat
Wheat
Legume
Sorghum
Oilseeds
Sheep
Wheat
-$4
$351
$186
$501
$316
-$275
-$753
Dairy-H
-$3,820
-$3,764
$614
-$15,098
$1,965
-$4,856
$791
$1,166
$859
$1,166
$500
$550
-$3,820
-$3,764
-$5,672
-$15,098
$1,965
$2,346
-$687
$952
$678
$952
$500
$0
-$4
$351
$252
$501
$310
-$275
-$753
$0
$0
-$3,319
$0
$0
$0
-$687
$1,059
$769
$1,059
$500
-$4
$351
$218
$501
$269
-$275
-$785
-$3,820
$4,972
-$3,764
$5,141
$4,314
$4,314
-$23,373
-$16,714
$1,965
$1,965
-$4,856
-$1,944
$8,783
$5,030
$2,517
$1,091
$2,004
$790
$2,517
$1,091
$726
$726
$0
$550
$461
-$89
-$53
$257
$118
$173
-$353
$291
$291
$135
$45
-$321
-$428
-$913
-$660
-$3,820
-$3,764
-$3,319
-$4,856
-$4,856
-$4,856
-$687
$953
$678
$953
$500
$0
-$4
$351
$247
$501
$310
-$275
-$803
-$3,820
$6,154
$6,154
$4,417
-$5,496
$3,649
-$3,764
$6,449
$6,500
$4,770
-$5,439
$3,918
$4,314
$4,314
$4,314
$4,314
$4,314
$4,314
-$23,373
-$16,714
-$5,552
-$14,446
-$14,446
-$14,446
$1,965
$1,965
$3,965
$289
$289
$289
$2,346
-$4,856
$2,346
$671
$671
-$8,485
$8,783
$5,030
$1,277
-$440
-$440
-$440
$1,092
-$334
-$410
$563
$0
-$983
$789
-$423
-$489
$255
$0
-$983
$1,092
-$334
-$410
$563
$0
-$983
$726
$726
$726
$248
$0
-$983
-$40
$23
-$40
$58
-$157
$58
$257
$276
$118
$358
$110
$258
$211
$250
-$130
$257
$9
$120
$291
$291
$314
$336
-$439
$336
$211
$25
-$42
-$130
-$247
-$130
-$321
-$326
-$348
-$180
-$198
-$85
-$873
-$299
-$749
-$1,079
$648
$1,108
$3,649
$3,649
$3,649
$3,649
$4,766
$3,918
$3,918
$3,918
$3,918
$5,035
$520
$2,243
$4,756
$2,243
$2,243
-$14,446
-$6,531
-$14,446
-$14,446
-$10,330
-$1,056
-$6,531
$1,619
$289
$1,406
-$382
-$6,531
-$13,459
-$6,531
$671
-$2,355
-$440
-$440
-$440
$4,560
$0
$563
$0
$563
$0
$0
$255
$0
$255
$0
$0
$563
$0
$563
$0
$0
$248
$0
$248
$0
$0
$0
$511
$737
$564
$558
$550
$681
$233
$558
$943
-$157
$58
-$157
$58
-$157
$110
$258
$24
$258
$530
$48
$120
$19
$120
$303
-$439
$336
$218
$336
$218
-$247
-$130
$55
-$130
-$247
-$200
-$147
-$239
-$275
$9
$327
$320
$113
$177
-$19
$4,417
$3,649
$4,678
$2,532
$2,243
$5,798
-$11,836
-$14,446
$1,406
-$6,909
-$6,531
$671
-$440
$9,502
-$545
-$1,365
-$739
-$1,365
-$545
-$1,365
-$134
-$1,365
$0
$0
-$157
-$157
$110
$110
-$27
$9
$218
-$439
-$247
-$247
-$240
-$201
-$2,267
$515
Table 10 Return per hectare Dry
CMAs
Condamine
Border Rivers
QLD
Warrego Paroo
Namoi
Central West
Maranoa
Balonne
Border Rivers
Gwydir
Western
Lachlan
Murrumbidgee
North East
Murray 1
Goulburn
Broken
Murray 2
North Central
Murray 3
Mallee
Lower Murray
Darling
SA MDB
Grapes
-$831
Stone
FruitH
-$21,106
Stone
Fruit-L
-$1,680
Pome
Fruit
-$4,876
Veg
$4,741
Cotton
Flex
$500
Cotton
Fixed
$859
Cotton/
Chickpea
-$528
Dryland
Cotton
$107
Rice
PSN
-$550
Dryland
Wheat
-$161
Wheat
$141
Wheat
Legume
$534
Sorghum
$205
Oilseeds
$88
Sheep
Wheat
-$695
DairyH
-$1,210
Dairy-L
-$1,199
-$3,764
$0
-$3,764
$3,217
-$7,117
-$4,764
$2,377
$2,377
-$21,106
-$20
-$29,153
-$23,720
-$1,680
$0
-$1,680
-$1,680
-$3,303
-$20
-$4,876
-$7,164
$4,741
$9,596
$4,741
$4,741
$500
$500
$726
$726
$678
$769
$2,004
$790
-$393
-$462
-$520
-$1,391
$107
$107
$298
$298
$0
$0
-$550
-$152
-$161
-$161
-$247
-$204
$141
$141
$26
-$92
$534
$534
$442
$277
$205
$205
$39
$39
$82
$41
-$57
-$153
-$695
-$695
-$704
-$771
-$1,210
-$1,151
-$1,362
-$1,121
-$1,199
-$1,106
-$1,361
-$1,119
-$3,840
-$3,764
-$4,764
-$4,876
-$4,856
-$4,876
$4,741
$500
$678
-$404
$107
$0
-$161
$141
$534
$205
$82
-$695
-$1,201
-$1,197
-$3,840
$2,435
$2,435
$697
-$5,516
$83
-$3,764
$4,394
$4,445
$2,715
-$5,439
$1,948
$2,377
$2,377
$2,377
$2,377
$2,377
$2,377
-$29,153
-$23,720
-$10,692
-$20,546
-$20,546
-$20,546
-$1,680
-$1,680
$320
-$3,355
-$3,355
-$3,355
-$3,303
-$4,876
-$3,303
-$4,978
-$4,978
-$13,804
$4,741
$4,741
$4,741
$3,066
$3,066
$3,066
$726
$726
$726
$248
$0
-$983
$789
-$423
-$489
$255
$0
-$983
-$439
-$376
-$932
-$947
$0
-$983
$298
$298
$298
-$180
$0
-$983
$0
$0
-$98
-$29
-$138
-$108
-$199
-$147
-$199
-$131
-$317
-$131
$26
$45
-$92
$106
-$116
$16
$442
$463
$277
$565
$289
$448
$39
$39
$31
$21
-$439
$21
-$17
-$127
-$255
-$358
-$475
-$358
-$704
-$728
-$627
-$527
-$456
-$279
-$1,324
-$808
-$1,197
-$1,533
-$34
$426
-$1,321
-$865
-$1,194
-$1,012
-$510
$591
$83
$83
$83
$83
$1,200
$1,948
$1,948
$1,948
$1,948
$3,065
-$1,210
$513
$3,026
$513
$513
-$20,546
-$6,551
-$20,546
-$20,546
-$16,430
-$4,608
-$6,531
-$2,117
-$3,355
-$2,239
-$6,902
-$6,551
-$17,578
-$6,551
-$4,978
$4,182
$3,066
$3,066
$3,066
$5,066
$0
$248
$0
$248
$0
$0
$255
$0
$255
$0
$0
-$1,110
$0
-$1,110
$0
$0
-$180
$0
-$180
$0
-$550
-$15
-$352
-$108
-$301
-$317
-$131
-$317
-$131
-$317
-$116
$16
-$194
$16
$220
$289
$448
$191
$448
$798
-$439
$21
-$96
$21
-$96
-$475
-$358
-$161
-$358
-$475
-$460
-$391
-$464
-$621
-$418
-$277
-$279
-$470
-$406
-$591
-$284
-$107
-$474
-$234
-$596
$697
$83
$2,623
$562
$513
$4,337
-$17,456
-$20,546
-$2,239
-$9,665
-$6,551
-$4,978
$3,066
$3,066
-$134
-$1,365
-$739
-$1,365
-$1,484
-$1,365
-$562
-$1,365
$0
$0
-$317
-$317
-$116
-$116
$289
$289
-$96
-$439
-$475
-$475
-$532
-$460
-$2,567
$10
-$2,765
-$238
CitrusH
-$3,840
CitrusL
-$3,764
-$3,840
-$20
-$3,840
$1,489
Table 11 Return per hectare Wet
CMAs
Condamine
Border Rivers
QLD
Warrego
Paroo
Namoi
Central West
Maranoa
Balonne
Border Rivers
Gwydir
Western
Lachlan
Murrumbidgee
North East
Murray 1
Goulburn
Broken
Murray 2
North Central
Murray 3
Mallee
Lower Murray
Darling
SA MDB
Grapes
$3,419
Stone
FruitH
-$9,148
Stone
Fruit-L
$9,122
Pome
Fruit
-$4,876
Veg
-$15,122
Cotton
Flex
$1,066
Cotton
Fixed
$859
Cotton/
Chickpea
$1,066
Dryland
Cotton
$499
Rice
PSN
-$650
Dryland
Wheat
-$650
Wheat
$399
Wheat
Legume
$124
Sorghum
$529
Oilseeds
$430
Sheep
Wheat
-$90
Dairy-H
-$15
-$3,864
-$2,867
-$9,148
$9,122
$13,549
-$15,122
$852
$678
$852
$285
-$100
-$100
$399
$220
$529
$424
-$90
-$15
-$20
-$3,840
$8,391
-$100
-$3,864
$8,850
-$514
$8,097
$8,097
-$20
-$17,658
-$9,757
-$100
$9,122
$9,122
-$20
-$4,876
$8,401
-$10,266
-$15,122
-$15,122
$959
$2,417
$991
$769
$2,004
$790
$959
$2,417
$991
$392
$1,671
$384
-$100
-$650
$687
-$100
-$650
$363
$399
$305
$146
$171
$104
-$575
$529
$303
$303
$383
$232
$143
-$90
-$155
-$281
-$452
-$227
$86
-$3,840
-$3,864
-$514
-$4,876
-$4,956
-$4,876
-$15,122
$853
$678
$853
$285
-$100
-$100
$399
$212
$529
$424
-$90
-$378
-$3,840
$9,809
$9,809
$8,059
-$5,516
$7,137
-$3,864
$10,420
$10,471
$8,730
-$5,539
$7,708
$8,097
$8,097
$8,097
$8,097
$8,097
$8,097
-$17,658
-$9,757
-$469
-$8,401
-$8,401
-$8,401
$9,122
$9,122
$11,122
$7,447
$7,447
$7,447
$13,549
-$4,876
$13,549
$11,874
$11,874
$2,057
-$15,122
-$15,122
-$15,122
-$16,797
-$16,797
-$16,797
$992
-$434
-$510
$463
-$100
-$1,083
$789
-$423
-$489
$255
$0
-$983
$992
-$434
-$510
$463
-$100
-$1,083
$381
-$903
-$1,005
-$208
-$100
-$1,083
-$100
-$100
$742
$996
$804
$803
-$100
-$100
$413
$646
$472
$468
$305
$324
$146
$415
$154
$311
$163
$216
-$244
$210
-$38
$44
$303
$303
$330
$356
-$539
$356
$325
$101
$64
-$16
-$133
-$16
-$155
-$150
-$233
-$31
-$94
-$13
-$174
$679
-$65
-$351
$2,522
$2,982
$7,137
$7,137
$7,137
$7,137
$8,254
$7,708
$7,708
$7,708
$7,708
$8,824
$3,886
$5,612
$8,129
$5,612
$5,612
-$8,401
-$6,551
-$8,401
-$8,401
-$4,285
$5,918
-$6,631
$8,957
$7,447
$8,564
$12,568
-$6,551
-$5,321
-$6,551
$11,874
-$15,680
-$16,797
-$16,797
-$16,797
-$13,797
-$100
$463
-$100
$463
-$100
$0
$255
$0
$255
$0
-$100
$463
-$100
$463
-$100
-$100
-$208
-$100
-$208
-$100
-$650
$941
$449
$803
$1,304
-$650
$592
$145
$468
$952
$154
$311
$66
$311
$616
$24
$44
$19
$44
$185
-$539
$356
$238
$356
$238
-$133
-$16
$164
-$16
-$133
-$96
-$50
-$151
-$127
$198
$1,809
$1,771
$1,488
$1,545
$1,296
$8,059
$8,640
$5,612
-$6,273
$8,564
-$6,551
-$16,797
-$645
-$645
-$1,229
-$100
-$100
$154
-$86
$238
-$133
-$120
-$2,267
$7,137
$6,322
$8,628
-$8,401
-$1,547
$11,874
-$16,797
-$1,465
-$739
$1,365
-$1,465
-$1,465
-$100
-$100
$154
-$38
-$539
-$133
-$96
$1,494
CitrusH
-$3,840
CitrusL
-$3,864
-$3,840
D
Limitations of the model
The limitations of the model include:







Changes in long term prices and costs are not considered;
The use of a static simulation approach - the unit of time is a year. There would be
advantages in disaggregating time to represent seasonality in the use and supply of
water;
The need for further spatial disaggregation of the basin scale;
Environmental assets such as wetlands located along the river systems are not
accounted for simplistically in this model. All environmental assets are modelled
within a catchment with the exception of the Coorong which is explicitly modelled ;
Lack and age of some commodity production data for each of the regions;
Changes in dam release rules are not considered; and
Changes in long term water supply are not considered.
Altering inflows and their reliability due to climate change scenarios can be simulated
using this model; however, it is not a focus of the analysis for this project.
A detailed list of all sources of data and adjusted data used in this model is provided in the
Bibliography.
Australian Government Land and Coasts 2010, What is a Natural Resource Management region?
Chambers, RG & Quiggin, J 2000, Uncertainty, production, choice, and agency : the state-contingent
approach, Cambridge University Press, New York.
MDBC 2001, Basin Salinity Management Strategy 2001-2015, Murray Darling Basin Commission,
Canberra.
—— 2005, Basin Salinity Management Strategy Operational Protocols, Murray-Darling Basin
Commission, Canberra.
O'Donnell, CJ & Griffiths, WE 2006, 'Estimating State-Contingent Production Frontiers', The American
Journal of Agricultural Economics, vol. 88, no. 1, pp. 249-66.
Rasmussen, S 2006, 'Optimizing Production under Uncertainty: Generalization of the StateContingent Approach and comparison with the EV Model', Royal Veterinary and Agricultural
University, Food and Resource Economic Institute, Unit of Economics Working papers FOI 3,
<www.foi.life.ku.dk/Publikationer/~/media/migration%20folder/upload/foi/docs/publikationer/wor
king%20papers/2006/5.pdf.ashx >.