The understanding of how allocation decisions can maximise the economic returns to the community from water for irrigation has received little attention, but is a significant issue for regional councils, those interested in water allocation policy development, and for irrigated farmers. There is a tradeoff between the amount of irrigated area and the reliability with which it can be undertaken. Overseas studies have generated a curve with optimum levels of allocation which maximise the economic return to the community from the resource. The study on which this paper is based used a single case study to model the individual and regional economic outcomes for four scenarios of water allocation, using daily time step simulation models of the hydrological, irrigation, farm and financial systems over the 1973 – 2000 period. The results show that there is an increasing return to the region as the allocation from the resource increases, at the expense of lower returns to existing users.Irrigation, reliability, regional economic impacts, Agribusiness, Agricultural and Food Policy, Agricultural Finance, Community/Rural/Urban Development, Environmental Economics and Policy, Farm Management, Financial Economics, Institutional and Behavioral Economics, Land Economics/Use, Resource /Energy Economics and Policy,

Bright, John

Bywater, Tony

Doak, Murray

Ford, Stuart

Harris, Simon

Thorrold, Bruce S.

English

Research Papers in Economics

 
Harris Consulting, July 2005 
 
 
 
 
 
 
 
 
 
Regional Economic Implications of 
Water Allocation and Reliability 
 
 
Simon Harris 
Harris Consulting 
e-mail: simon@harrisconsulting.co.nz 
 
Murray Doak 
MAF Policy 
 
Stuart Ford 
The Agribusiness Group 
 
Tony Bywater, 
Lincoln University 
 
John Bright 
Aqualinc 
 
Bruce Thorrold 
Dexcel 
 
 
 
Paper presented at the 2005 NZARES Conference 
Tahuna Conference Centre – Nelson, New Zealand. August 26-27, 2005. 
 
Copyright by author(s). Readers may make copies of this document for non-commercial 
purposes only, provided that this copyright notice appears on all such copies. 
 
  
Harris Consulting, July 2005 
Regional Economic Implications of Water Allocation and 
Reliability 
 
Simon Harris (Harris Consulting) 
simon@harrisconsulting.co.nz 
Murray Doak (MAF Policy) 
Stuart Ford (The Agribusiness Group) 
Tony Bywater, (Lincoln University) 
John Bright, (Aqualinc) 
Bruce Thorrold, (Dexcel) 
 
 
 
 
 
Abstract 
The understanding of how allocation decisions can maximise the economic returns to 
the  community  from  water  for  irrigation  has  received  little  attention,  but  is  a 
significant  issue  for  regional  councils,  those  interested  in  water  allocation  policy 
development, and for irrigated farmers. There is a tradeoff between the amount of 
irrigated area and the reliability with which it can be undertaken. Overseas studies 
have  generated  a  curve  with  optimum  levels  of  allocation  which  maximise  the 
economic return to the community from the resource. The study on which this paper 
is based used a single case study to model the individual and regional economic 
outcomes for four scenarios of water allocation, using  daily time step simulation 
models of the hydrological, irrigation, farm and financial systems over the 1973 – 
2000 period.  The results show that there is an increasing return to the region as the 
allocation from the resource increases, at the expense of lower returns to existing 
users.  
 
Key words: Irrigation, reliability, regional economic impacts. 
 
 
Background 
Water is an important resource for many stakeholders, having value both in stream 
and out of stream.  The allocation of water has obvious environmental dimensions, 
but it has received little attention in terms of understanding how allocation decisions 
can maximise the economic returns to the community from the resource.   
 
At a given minimum flow in a river, there are different levels of allocation which can 
be made to users.  As more water is allocated, the allocation becomes less reliable 
because  the  river  is  less  frequently  at  the  flows  required  to  sustain  the  total 
allocation.  There is therefore a trade off between the amount of irrigation which can 
take place and the reliability with which it can be undertaken – less area irrigated 
more reliably, or more area irrigated at lower reliability.  
 
Overseas studies have generated curves with “optimum” levels of allocation which 
maximise the economic return to the community from the resource.  Three studies 
(Verdich and Bryant; Jones, Musgrave and Bryant 1992; Dudley and Hearn, 1993)  
Harris Consulting, July 2005 
which directly tried to assess this trade off, each indicated that the community net 
benefits were optimised at a level of reliability lower than the level which optimised 
the  individual  net  benefit.    Net  benefit  to  the  community  was  increased  at  the 
expense  of  an  increased  variability  to  the  individual  and  in  some  cases  to  the 
community. 
 
Reliability can be characterised (Kuczera, 1987) by the probability of a failure, its 
duration,  and  its  magnitude.  Predictability  of  failure  may  also  be  an  important 
descriptor from a user point of view. The concepts of reliability can also be extended 
to include characteristics of the receiving use – the vulnerability of the receiving 
system to the water loss and its resilience in terms of its ability to recover following 
loss.  Incorporating all these dimensions of reliability into a single measure can be 
difficult, and irrigation manager and policy description of reliability need to relate to 
users perceptions of reliability.   
 
A number of attempts have been made to develop indicators of reliability (e.g. Robb 
and McIndoe, 2001), although it is not clear the degree to which these have proven 
useful for allocation purposes.  Furthermore the use of indicators does not allow 
quantification of changes which occur, and therefore the point at which allocation 
becomes  optimal.    Little  other  work  on  assessing  the  economic  implications  of 
irrigation reliability appears to have been undertaken in the NZ context.  
 
Method 
This study has used a modeling approach to determine the regional and individual 
farm economic outcomes for four scenarios of water allocation from the Rangitata 
River, with all scenarios having the same minimum flow regime.  The four scenarios 
were: 
 
  a very reliable
1 allocation, (irrigating 29,000 ha) 
  the current allocation, (irrigating 64,000 ha) 
  a  less  reliable  allocation,  with  an  additional  26%  of  water,  (irrigating 
81,000 ha) 
  a least reliable allocation, with an additional 59% of water.  (irrigating 
102,000 ha) 
 
The modeling used daily time steps for 1973 – 2000 period, with simulations run in 
sequence through  
  a hydrological model (John Bright, Aqualinc, pers comm.) 
  an irrigation scheduling model (John Bright, Lincoln Environmental, pers 
comm.) 
  7 farm systems models (Cacho et al, 1999; Thorrold et al, 2004; Bright, 
Aqualinc, pers comm.)  
  financial models (Ford, The Agribusiness Group, pers.comm.) 
The arrangement of the models is shown in Figure 1. 
 
                                                 
1 Refers to the availability of water on farm.  In fact Scenario 1 has twice as much water allocated to it 
at  the  same  reliability  as  Scenario  2,  so  the  probability  that  some  water  will  be  available  is 
significantly higher.  Using this approach allowed us to also test the impact of the amount of water 
applied on the overall outcome.  
Harris Consulting, July 2005 
Figure 1: Outline of Water Reliability Study 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
RESULTS 
The impact on production from lower reliability is significant, but does not appear to 
be proportionate to the loss in water.  This is demonstrated in Figure 2 below which 
shows the relationship between pasture production in the most reliable scenario and 
that of the least reliable scenario.  It demonstrates that the relationship between the 
amount of water applied over a year, and the pasture production is not linear – in fact 
the loss in pasture is only about 50% of the loss in water.  The impact on production 
is  even  less  in  the  case  of  cropping,  and  this  appears  to  go  some  way  toward 
explaining the relatively small differences in financial outcomes between scenarios.  
 
Optimum 
stocking rate and 
policy (expert) 
Crops grown 
(expert) 
Feedback via 
pasture cover  
Feedback 
via ET   Feedback  
via ET  
Irrigation scheduler 
Crop growth 
(LE) 
Pasture growth (LE, 
Dexcel and LU) 
Animal output (sheep Lincfarm, 
deer and beef finishing 
estimated, dairy (Dexcel)  
Farm financial 
models (Cash Farm 
Surplus) 
Feedback via 
crop priority 
Regional Cash Farm 
Surplus  
River Hydrology 
Model 
Land use mix (expert)  LE – Lincoln Environmental models 
LU – Lincoln University Lincfarm Model 
Dexcel -  Whole Farm Model  
Harris Consulting, July 2005 
Figure 2: Relationship between water applied and pasture yield: Least reliable 
scenario as a proportion of Most reliable scenario 
 
 
Cash farm surplus (CFS)
2 results were generated for each farm model for the four 
scenarios.  CFS decreases for all models from Scenario 1 to 4 (highest to lowest 
reliability).  The results for the irrigated sheep and dairy farms are shown in Figure 3 
and Figure 4 below, and exhibit low variability (coefficient of variation (CV) 4%  - 
14%).  The arable models are intermediate in variability, and the dryland models are 
highest variability at 28% - 51%.  The minimum CFS, or the worst year that farmers 
would face is in the order of 11%  - 13% worse for the scenarios which are less 
reliable than the current allocation for the irrigated sheep model, 4%  - 9% worse for 
the dairy models, and 1% to 3% worse for the crop models. The CV increases when 
capital costs of irrigation development are taken into account. 
                                                 
2 CFS = Gross Farm Revenue – Farm Working Expenses.  It does not include tax, interest, drawings, 
depreciation etc. 
Pasture production relative to irrigation: Most vs least reliable scenarios
y = 0.432x + 0.5845
R
2 = 0.5925
0.70
0.75
0.80
0.85
0.90
0.95
1.00
0.70 0.75 0.80 0.85 0.90 0.95
Irrigation applied (least/most reliable)
P
a
s
t
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r
e
 
y
i
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l
d
 
(
l
e
a
s
t
/
m
o
s
t
 
r
e
l
i
a
b
l
e
) 
Harris Consulting, July 2005 
Figure 3: Sheep Finishing Breeding (irrigated) CFS/ha 
 
 
Figure 4: Dairy Spray Irrigated CFS/ha 
 
 
Analysis of the CFS results suggests that no land uses are likely to be precluded 
because the extra variability is not significant enough to limit land use options.  The 
average and minimum CFS figures are lower with lower reliability, suggesting that 
the land values for systems under less reliable scenarios will be lower. 
 
-200.00
-100.00
-
100.00
200.00
300.00
400.00
500.00
600.00
1973
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Year
$
/
h
a
Scenario 1
Scenario 2
Scenario 3
Scenario 4
Dryland
0
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Years
$
/
h
a
Scenario 1
Scenario 2
Scenario 3
Scenario 4 
Harris Consulting, July 2005 
Figure  5:  Average  Annual  Cash  Farm  Surplus  Aggregated  for  all  Farms  in 
Study Area 
 
 
The results were aggregated for the whole command area based on surveyed land use 
mixes.  This aggregation shows that the increasing allocation results in increasing 
Total CFS in the region (heavy dashed line, Figure 5, „Average‟ row, Table 1).  The 
increase in economic return to the region remains true whether or not capital costs of 
new investment in irrigation are included (shaded line with crosses, Figure 5). The 
regional variability increases in absolute terms, but as a proportion of the increased 
average CFS it remains stable.  CFS without capital costs can be used as a proxy for 
direct estimates of contribution to Gross Domestic Product (GDP), and CFS with 
capital  costs  can  be  used  as  a  measure  of  net  benefit.  Sensitivity  testing  with 
increased feed costs ($0.20/kgDM) and -20% on price assumptions did not change 
the overall shape of the curve.  
 
Table 1: Aggregated Cash Farm Surplus for whole command area 1973 – 2000 
(without capital costs) 
Test  Scenario 1  
(most reliable) 
Scenario 2 
(current) 
Scenario 3  Scenario 4 
(least reliable) 
Mean  $65,000,000  $100,000,000  $127,000,000  $158,000,000 
Change in average (relative to current)  65%  100%  126%  157% 
Minimum  $38,000,000  $79,000,000  $105,000,000  $128,000,000 
Coefficient of variation (CV  = SD/Mean)  12%  9%  8%  9% 
 
Increasing  reliability  relative  to  the  current  situation  results  in  lower  economic 
returns     (-35%) to the region in terms of total CFS, and an increased variability 
when expressed as a proportion of the average. These changes arise because more 
dryland area is created within the study area to accommodate the smaller irrigated 
area which can be serviced at an increased reliability. 
$0
$20,000,000
$40,000,000
$60,000,000
$80,000,000
$100,000,000
$120,000,000
$140,000,000
$160,000,000
$180,000,000
0 10 20 30 40 50 60
Equivalent Irrigation Allocation (cumecs)
R
e
g
i
o
n
a
l
 
C
F
S
Region Total without
capital costs
Regional total with capital
costs for new irrigation
Existing Area
Dryland 
Harris Consulting, July 2005 
 
The  existing  irrigated  area  experiences  a  decrease  in  aggregate  CFS  with  both 
increasing and decreasing reliability from the current allocation (Boxed line: Figure 
5; Mean row: Table 2).  Its variability also increases in all scenarios other than the 
current situation (64,000 ha irrigated).   
 
Table 2: Annual Cash Farm Surplus for Existing RDR Area  
  Scenario 1  
(most 
reliable) 
Scenario 2 
(current) 
Scenario 3  Scenario 4 
(least 
reliable) 
Mean  $56,000,000  $91,000,000  $88,000,000  $85,000,000 
Change in average (relative to current)  61%  100%  97%  93% 
Minimum  $42,000,000  $75,000,000  $73,000,000  $67,000,000 
Coefficient of variation (CV  = SD/Mean)  8%  7%  9%  11% 
 
 
 
DISCUSSION 
The results from this project point to an increased allocation increasing regional GDP 
and  a  net  regional  benefit,  with  a  penalty  to  those  existing  irrigators  from  the 
resource and a gain to new irrigators converting from dryland.  The changes we have 
introduced  in  terms  of  allocation  are  extreme,  simply  because  more  moderate 
changes to allocation resulted in differences which were too small to be resolved by 
the modeling. In this case study resource environmental constraints would limit the 
allocation before the upper allocation limits tested here.  The conclusions regarding 
the increased regional GDP appear robust, with the trends consistent across all model 
results.  The conclusions regarding net benefit are likely to be sensitive to the capital 
costs of irrigation development.   
 
The results reflect a particular resource and relate mainly to regional impacts.  The 
implications  for individuals,  particularly existing irrigators, is  more severe in  the 
extreme scenarios. In particular the decrease in reliability modeled here appears to 
increase the vulnerability of farms to changes in other factors such as product prices.  
As a result there are equity implications which need to be taken into account with 
significant  increases  in  water  allocation  at  the  current  minimum  flow  from  this 
resource.  
 
In methodological terms the approach has been proven as feasible, although complex 
and not well understood by potential users.  Unfortunately the range of reliability 
studied here was not sufficiently great to preclude specific land uses.  As a result we 
have not been able to answer questions regarding the relative importance of land use 
feasibility  vs.  the  adverse  effect  of  poor  reliability  in  estimating  the  impacts  of 
allocation on the community returns from a resource. 
 
The model development process has been very extensive, and the individual model 
components  have  been  tested  against  actual  farm  trials  and  they  are  reasonably 
capable  of  replicating  production  systems  in  tests  of  that  nature.    However  the 
models  are  inevitably  limited  in  the  extent  to  which  they  are  able  to  replicate  
Harris Consulting, July 2005 
physical  and  management  systems.    Farmers  involved  in  the  development  and 
specification  of  this  project  expressed  concern  over  the  ability  of  the  models  to 
accurately represent farm systems. 
 
The models require further work in reflecting management input into the physical 
systems, particularly in relation to management of variability.  The models operate 
under a reasonably constant management system because this has to be set to allow 
the production system to operate regardless of the conditions it encounters.  In the 
case of the dairy model this includes maintaining pasture production constant and 
varying feed inputs.  In this way the model management may be more conservative, 
and will certainly be less sophisticated than an actual farm manager.  While the level 
of conservatism was tested by changing the stocking rate plus or minus 2 stock units, 
the matching of demand to supply may operate at a more sophisticated level in the 
actual management of a farm.  For this reason the actual variability experienced by a 
farmer may be greater – because farm managers are better able to take advantage of 
upside variability, or may be less, because farm managers are likely to be better at 
managing downside variability than the models.  We are unable to define how actual 
management would relate to the model management, but it should be noted as an 
area for concern. 
 
This approach to modeling reliability has taken considerable time, and has proven 
complex and not altogether transparent to users.  However it has produced a set of 
results  which  give  a  definitive  answer  to  the  questions  of  whether  an  increased 
allocation in the case study area would increase regional GDP and produce a net 
benefit.  Work should continue on developing and testing this methodology.  This 
work could include: 
  Development of the farm models to better reflect farm systems, and importantly 
the management inputs into those farm systems, so that models can be run under 
conditions  of  variability  yet  reflect  in  a  reasonable  fashion  the  types  of 
management interventions which are used to optimise the farm system under that 
variability. 
 
  Applying the method to an expanded range of situations.  It should be applied to 
more resource types, preferably in conjunction with a river classification system 
which  enables  the  work  to  be  replicated  by  class  rather  than  for  each  river.  
Equally importantly the resource  allocations  need to  be “stress tested” to  the 
point where land uses are no longer viable, so that we can understand why that 
happens and how the overall returns to the region change as allocations approach 
that point.   
 
 
Acknowledgements 
This work was funded by MAF Policy and Environment Canterbury.  The project 
team has included Grant McFadden, John Greer, Malcolm Miller and John Glennie.  
Many thanks to the farmers and other participants who assisted through the project.   
 
References 
Cacho, O.J., Bywater, A.C., Dillon, J.L. „Assessment of production risk in grazing 
models” Agricultural Systems, 60 (1999) 87 – 98.  
Harris Consulting, July 2005 
 
Dudley, N.J. and Hearn, A.B.  1993.  „Systems Modeling to Integrate River Valley 
Water  Supply  and  Irrigation  Decision  Making  Under  Uncertainty‟.    Agricultural 
Systems, 42 (1993) 3 – 23. 
 
Kuczera,  G.  1987.    „Review  on  risk  and  reliability‟,  National  Water  Research 
Seminar:  Discussion  Papers,  Australian  Water  Research  and  Advisory  Council, 
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Jones, R.  Musgrave, W. and Bryant, M.  „Water Allocation and Supply Reliability in 
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Robb, C.; McIndoe, I. 2001.  „Reliability of Supply for Irrigation in Canterbury‟.  
Lincoln  Environmental  Contract  Report  No  4465/1  prepared  for  Environment 
Canterbury.   
 
Thorrold, B.S.; Bright; K.P., Palmer, C. A.; Wastney, M.E..  2004.  „Modelling the 
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Verdich,  P  and  Bryant,  M.  ~1985  „Optimal  Reliability  of  Irrigation  Supply  –  a 
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Assessment of production risk in grazing models”

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Systems Modeling to Integrate River Valley Water Supply and Irrigation Decision Making Under Uncertainty‟.

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Regional Economic Implications of Water Allocation and Reliability

Regional Economic Implications of Water Allocation and Reliability

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