Action 6: Water Full cost & Irrigation Cost recovery
Sub-Action 6.2: Economic Analysis (of water pricing).
Case study:
A Ranking of water pricing methods for the Pinios
Local Organization of Land Reclamation (LOLR)
Vasilaki, A, Kampas, A. , Rozakis.S., Papadas, C.
Agricultural University of Athens
January 2013.
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Table of Contents
6.2.1. Water Pricing
6.2.1.1. Volumetric Pricing Methods
6.2.1.1.1 Uniform Rate pricing.
6.2.1.1.2. Multiple Rate pricing
6.2.1.2 Non-volumetric pricing Methods
6.2.2 Brief Synopsis of the empirical literature
6.2.3. Modelling Framework
6.2.4. Water Pricing Scenarios
6.2.5 Results
6.2.6. Conclusions
6.2.7. References
Executive Summary
This section examines the relative performance of three possible water pricing methods,
namely the land-based charge, the average cost pricing and the two-part tariff charge.
The rationale for such a comparison is that a land-based charge is very often used in
practise, the average cost pricing is usually proposed when cost recovery is the main
policy objective, while the two-part tariff methods combines the rationales of cost
recovery and economic efficiency. On the basis of the derived results, the average cost
pricing outperforms all other pricing alternatives examined both on economic and
environmental grounds. Such a result has important policy implications since may guide
water policy formation towards the more effective water management regimes.
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6.2.1 Water Pricing
Water Framework Directive (2000/60/EC) indicates that water pricing is among the policy
instruments for water resources conservation. It is considered to be the most suitable
measure since not only preserves the quality and quantity of water but it also serves its
efficient use (Johansson 2000).
Water pricing seems to have a triple role as financial, economic and environmental tool.
First, as a financial tool, it aims to recover the financial cost of water (investment, operation
and maintenance costs) (Dinar and Saleth 2005; Molle and Berkoff 2007; Perry 2009).
Second, as an economic tool, water pricing by subsuming the scarcity rents of water
achieves efficient use through: a) its better management and preservation, b) the cultivation
of less water demanded crops and the investments on limited water consumption
technologies, and c) the redistribution of water to high valued uses (Cornish, Bosworth et
al. 2004; Dinar and Saleth 2005; Molle and Berkoff 2007; Perry 2009). Third, as an
environmental tool, pricing provide the incentives for better quality water through the
reduction of pollution level.
Water pricing methods differ according to the prevailing financial and natural conditions
and the pre-specified policy objectives. A typical classification of pricing methods
comprises: a) volumetric methods, b) non volumetric and c) ad hoc methods. As Tsur,
Dinar et al. (2004) argue, the multiplicity of methods reflects the variability in conditions
and multiple criteria that underlie water allocation, with the main criterion underlying the
pricing of any scarce resource to be efficiency.
6.2.1.1. Volumetric pricing methods
Water bill under these methods is assessed on the basis of metered water consumption.
This requires accurate information about water consumed by every user and a central
administrative service, which sets the price rates and collects the payments (Bosworth,
Cornish et al. 2002). The volumetric methods may use uniform of multiple rates. The first
category includes marginal and average cost pricing methods and the second contains two
– part pricing, variable unit or non – linear pricing and block rate pricing.
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6.2.1.1.1 Uniform rate pricing
Marginal cost pricing
The marginal cost pricing is the most common volumetric pricing method in the literature,
under which the price of water equals the marginal cost of water supply. This pricing
method achieves efficient water allocation in the sense that it maximizes the joint surplus
of water users and water supply. The surplus of the water users (farmers) is given by area
A, while the surplus of water suppliers (water service) is given by area B in Figure 1. The
short-run (not including fixed costs) welfare is the sum of the two areas.
Figure 1: Marginal Cost Pricing
A
B
Source: Tsur, et al. (2004)
If the intersection point falls of the inverse derived water demand, pf   q  , and the
marginal cost is below the AC curve, MC  q  , as is the case under AC2 in Figure 1, then
w*< AC(q(w*)), implying that the operating profit of the water supplier is insufficient to
cover the fixed cost. In the long run, the water supplier will need to be subsidized to stay
in business. However, the subsidy of supply (when the MC curve lies below the AC curve),
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can cause inefficiency in other sectors of the economy through its reliance on public funds
to subsidize water supply. A question then arises regarding whether the water price should
be set so as to balance the budget of the water supplier, including the fixed cost. This leads
to the consideration of average cost pricing, where the price of water is set at the
intersection of the demand and average cost curves. Under such pricing, water proceeds
must equal total cost (recall that AC=TC/q). Such a situation is depicted in Figure 2, with
the average cost price represented by w#.
According to Dinar, et al. (1997) the major advantage of marginal cost pricing is efficiency.
Furthermore, under marginal cost pricing the underestimation of water’s value is avoided
and hence the notorious overconsumption may be controlled. Sampath (1992) argues that
marginal cost pricing contributes to better environmental management as farmers tend to
reduce irrigation costs and water consumption.
However, this pricing method has some notable disadvantages. Dinar, et al (1997) argue
that the estimation of marginal cost is data demanding and it differs for short and long
run. Therefore, its application is difficult and requires continuous monitoring which is too
costly and demanding. Sampath (1992) argues that marginal cost pricing doesn’t pay
attention on equity, income redistribution and rural development. Finally, if there are
market distortions, as asymmetric information or water scarcity or implementation costs
or water perception as a public good, the effectiveness of the method is also affected
(Johansson, et al. 2002).
Marginal cost pricing of water is applied in Jordan, Mexico, Morocco, Australia, India,
England, France, the U.S.A. and Israel Johansson (2000).
Average cost pricing
The average cost pricing is a volumetric pricing method, in which the price of water equals
its average cost. This pricing method put forward in order to recover full cost. So, average
cost pricing guarantees a balanced budget of the water supply, but entails an efficiency loss
in the irrigation sector (it decreases the joint welfare of farmers and water suppliers).
Moreover, the farmers carry most of the burden of the welfare loss.
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Figure 2: Average Cost Pricing
C
Source: Tsur, et al. (2004)
As Tsur et al. (2004) explain, moving from marginal cost pricing to average cost pricing
involves a shift from the quantity–price configuration {q(w*), w*} to {q(w#), w#}. Thus
the move from marginal cost to average cost pricing involves a loss of welfare given by the
area C (Figure 2). A move from marginal cost to average cost pricing, thus, makes water
suppliers better off (their gain exceeds their loss) and farmers much worse off. Moreover,
the loss exceeds the gain and the result is a net decrease in welfare.
6.2.1.1.2. Multiple Rate pricing
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Such a method utilises two different rationales in order to achieve the predetermined
objectives, namely cost recovery and efficiency. Water pricing with different rate structures
may achieve several social desires as efficiency, equity and revenue neutrality. Griffin
(2001) argues that the two part pricing may be designed to encourage customers to make
economically efficient consumption, continuation, and enrolment decisions, and at the
same time to balance the water utility budget.
The first component refers to new connections which are required to pay a one-time fee
designated by F . The second component refers to all connections which have to pay for
their metered water consumption according to the following billing formula:
Billn  M  p  (wn  w)
(1)
Formula (1) has three parameters that must be assessed: a) the wn is metered water
consumption and w is a threshold water quantity related to nature of water as a sui generis
good, b) the M is a fixed "meter charge" to be paid each period which aims to recover
the fixed costs, and c) p is the marginal cost of water supply.
Connections that consume exactly the threshold, the bill will consist only of the meter
charge. By contrast, connections consuming more than the threshold they will pay, in
addition to the meter charge, p for every unit of water exceeding the threshold. For
connections consuming less than the bill threshold, a credit will be generated by every unit
of water consumed below w . Should this credit exceed the meter charge, the customer
will receive a payment rather than a bill.
When parameterized correctly, this rate structure provides economically efficient
incentives for consumers. The general purpose of each part of the rate is as follows. F
induces efficient decisions by new customers and offers a means of cost recovery for
system extensions. M is a means of collecting costs which are functionally not related to
water processed (fixed costs). Hence, F and M address the efficient allocation of nonwater resources, although F will also include the cost of water acquisitions in water-scarce
regions.
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The purposes of p is to allocate water efficiently and to recover the variable costs. Lastly,
w is a budget-balancing parameter, introduced as a substitute mechanism for the average-
cost pricing of water, which is the traditional method of balancing utility budget.
Should the utility generate an economic surplus, it is dispersed uniformly across all
connections using this instrument. If optimal prices result in a net financial loss for the
utility, w will be negative and it serves to collect the shortfall from all connections equally.
The main advantages of this method refer to the total cost recovery and the reduction of
consumption (Griffin 2001; Easter and Liu 2005). On the other hand, Griffin (2001) refers
as disadvantage, the difficulty in application as it would be too complicated if the condition
of homogeneity in consumers, in quality of supplied water, and in the values of p , F and
M which differ according to season, is relaxed. Easter and Liu (2005) emphasize the
higher management cost of this method comparatively to the uniform rate methods. Two
– part pricing is applied in Brazil (Easter and Liu 2005), Jordan, Israel and in three Spanish
areas (Cornish, Bosworth et al. 2004).
Block - rate pricing
Block – rate pricing provide two or more prices for water used, where each price applies
to a customer’s use with a defined block. When prices rise within each successive block,
it’s called Increasing Block Tariffs (IBTs). Otherwise, if prices reduce within each
successive block, it’s called Decreasing Block Tariffs (DBTs). According to Boland and
Whittington (2003), in order to design a block–rate pricing structure, one must make three
kinds of decisions for each category of water use: to decide on the number of blocks, to
determine the volume of water use associated with each block, and to specify the prices to
be charged for water use within these blocks.
On the one hand, the main goal of block prices is to bring about water use reduction
without burdening the farmers (Bar-Shira, et al. 2006). In particular, there is concern that
marginal cost pricing in agriculture would crowd out family and small farming. By contrast,
an IBTs schedule imposing the high optimal price at the margin while maintaining a lower
average price, enhances equity. Bar-Shira and Finkelshtain (2000), showed that increasing
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block tariffs implements the second-best social objective of maximizing welfare subject to
a desired number of firms in the industry.
This pricing method presents major advantages. Specifically, it is often argued that an IBT
adresses social fairness (Liu, et al. 2003, Dudu and Chumi 2008). Another advantage is that
it gives the motivation to the users to reduce their consumption. Easter and Liu (2005)
propose such a method under special conditions such as water scarcity, low income
farmers and high water costs compared to farmers’ net income.
However, the setting of the initial block, the total number of blocks, and the water volume
within each block have to be assessed. Moreover, designing IBTs to promote cost recovery
leads to two significant difficulties: (1) utilities typically lack the information about user
demand needed to predict the revenue that any particular IBT will produce, and (2)
compromises between revenue collection and economic efficiency objectives may further
distort other functions of the tariff (Boland and Whittington 2003). This pricing method
is primarily used in areas with advanced system surveillance technology such as in Israel
and California (Bosworth, Cornish et al. 2002).
On the other hand, Decreasing Block Tariff (DBTs) is based on the economic concept
that the goods with high value should be more expensive. In terms of social fairness, this
method is considered unaccepted, as smaller consumers pay more than larger (Gracia,
Valinas-Garcia et al. 2001). However, this schedule also has some advantages as it provides
water service with sufficient revenues and it contributes to water conservation (Griffin
2006).
2. Non-volumetric pricing methods
These methods estimate the water bill independently of water consumption and they are
mainly used in agricultural sector. This category includes output pricing, input pricing and
area-based pricing.
Output pricing
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Output pricing method charges a price per unit of produced yield. This requires data about
volume of yields, but it is not necessary to meter the volume of water used (Johansson
2000). It is considered an easily applied method as the yield is directly estimated (Bosworth,
Cornish et al. 2002).
Input pricing
Accordingly to the input pricing methods, farmers pay for water indirectly. A kind of tax
is imposed on every unit of consumed input (Johansson 2000). It is considered an easily
applied method as the quantity of used inputs is directly estimated (Bosworth, Cornish et
al. 2002).
Area–based pricing
Area–based methods are the most common in agricultural sector. Farmers are obliged to
pay a fixed price per hectare of cultivated land, which often is dependent on the cultivated
crops, the frequency of irrigations and the irrigation method (Bosworth, Cornish et al.
2002). As Easter and Liu (2005) put it, a standard way to specify this price is by equating
it with the average operation and maintenance cost of the water service. Such methods are
easy to design and have low application costs. On the contrary, the immoderate
consumption of water remains its major disadvantage. Area–based pricing methods are
used in Spain (Cornish, Bosworth et al. 2004), China, India, Iraq, Mexico, Nigeria, Pakistan,
Peru, the Philippines, (Johansson 2000), Vietnam, Turkey, Argentina, Greece, Japan and
Sudan (Molle and Berkoff 2007).
The relative performance of different pricing methods is based on a number of criteria
such as cost recovery, environmental effectiveness (water consumption reduction),
economic effectiveness (rational distribution of water), equity-social fairness and
applicability. From the above discussion it is obvious that some methods are more suitable
than others in order to achieve one or more targets. Table 1 summarizes the anticipated
relative performance of different pricing methods.
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Table 1: The Relative Performance of Water Pricing Methods
Water pricing Methods
Use
Urban &
Irrigation Industrial
Examples
Cost
Recovery
Equity
Efficiency
Compatibility with
the Polluter Pays
Principle
Applicability
No
No
Yes
yes
demanding
A. Volumetric
1. Uniform
1.1.Marginal Cost
?
Yes
Jordan, Mexico,
Morocco, Australia,
India, England, France,
the U.S.A. and Israel
1.1.Average Cost
?
Yes
California (US),
Australia
Yes
No
No
yes
easy
?
Yes
Italy, Israel Greece,
Cyprus
?
Yes
?
yes
complex
?
Yes
?
?
Yes
?
yes
complex
?
?
Brazil, Jordan, Israel
Spain, Germany
yes
Yes
Yes
yes
demanding
No
India, Jordan, Greece,
China, India, Iraq,
Mexico
No
Yes
No
no
easy
2. Non-uniform
2.1. Increasing Block
Rates
2.2. Decreasing Block
Rates
2.3. Two part system
B. Non Volumetric
3. Area or crop based
Yes
Source: Adjusted from Loehman (2004)
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For example, if cost recovery is the main aim, then average cost pricing can be used. For
the reduction of water consumption, IBTs or block-rate pricing can be used. In order to
achieve the maximization of social welfare (economic effectiveness), it’s better to use the
marginal cost pricing. The aim of equity can be accomplished through block-rate pricing.
By contrast, if the criterion is design ease and applicability then non-volumetric and average
cost pricing are better options.
6.2.2. A brief synopsis of the empirical literature.
The pricing methods force farmers to adopt different behavioural strategies, such as
cropping patterns changes (extensive margin changes), irrigation technology changes and
reductions in the amount of inputs used (intensive margin changes). The relevant literature
is massive and increasingly sophisticated. A typical modelling choice of an application
which aims to access the relative performance of pricing methods comprise two parts: a)
the use of biophysical models to capture the natural complexity, and b) a mathematical
programming model to simulate the possible, normative in nature, induced decisions by
farmers. The list of mathematical programming models include: static models, discrete
stochastic models, positive mathematical programming, and dynamic models. Multicriteria Decision Making are often employed to capture multiple policy objectives whereas
efficiency analysis is performed through Data Envelopment Analysis.
The use of bio-economic modelling is very often used in practice to assess the relative
performance of different pricing methods (Ortega, de Juan et al. 2004; Mouratiadou,
Russell et al. 2008). These models are a combination of mathematical programming and
bio-physical agronomic simulation models, which have been developed to incorporate
agro-ecological and socio-economic data in the analysis of agricultural policy’s impacts in
sustainable land use (Ruben, Moll et al. 1998). The major advantage of coupling biophysical
an economic models is that bio-economic models can be tuned to simulate land
management decisions fairly accurately and hence they can be used for short- and longrun predictions (Janssen and van Ittersum 2007).
When these models are used to predict the impacts of water pricing, the results are often
presented in terms of farmers’ gross margin or income reductions, water demand changes
12 | P a g e
and the likely environmental impacts. See, for example. Ortega, et al.( 2004) and GarcíaVila et al (2009).
Mathematical programming models try to assess the optimal cropping pattern, which
maximizes the farmers’s gross margin or net income under some constraints (technical,
economic, financial and political).
Varela-Ortega et al. (1998) comparing a uniform volumetric pricing with a block rate
pricing found that the volumetric pricing results in greater reduction of farmers’ income,
but block rate pricing brings about a better water conservation.
The volumetric water pricing often results in reduction of farm income, reduction of
cultivated crops, reduction of labour demand and of fertilizers’ use and set-aside of
agricultural land. See, for example, Berbel and Gómez-Limón (2000) Doppler, et al. (2002)
and Scardigno and Bazzani (2008).
By contrast, a non-volumetric pricing method is expected to modify the composition of
irrigated and rain-fed crops and to induce set-aside (Djanibekov 2008).
Despite the wide use mathematical programming models in the literature their major
drawback refers to their normative rationale. More specifically, a valid criticism is often
raised concerning the optimization of a single objective as the major determinant that
drives agents behaviour, namely that of profit maximization. When farmers pursue
multiple objectives, then a Multi-criteria Decision Making (MCDM) framework is arguably
a better modelling choice.
Gomez-Limon and Riesgo (2004) suggest multi-criteria decision making for the policy
analysis of addressing water price changes. This method takes into consideration a lot of
the decision–making factors such as risk, dependence of hired labour, investments, fixed
costs, spare time and farmers’ debts (Hazell and Norton 1986).
Within such as a setting, volumetric pricing is found to reduce water use, farmers’ income,
labour demand, fertilizer use and it drives to suppliers’ gains (Gómez-Limón and Berbel
2000, Gomez_Limon, et al. 2002, Gómez-Limón and Martínez 2004, Bartolini, Bazzani et
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al. 2007). Gomez-Limon and Riesgo (2004) use MCDM to examine the impact of
volumetric pricing on nitrogen emission, while Manos, et al. (2007) examine the link
between water pricing and energy use. Saraiva and Pinheiro (2007) argue that the choice
of the appropriate pricing method is conditional to the pre-specified policy target. They
put forward quotas for consumption reduction, volumetric methods for rational use of
water and uniform rate pricing for cost recovery.
Finally, when the research emphasis is on the likely linkages between economic sectors the
Input–Output (I-O) method is often considered. This method does not rely on ex ante
behavioural assumptions and the emphasis is given to the productive process (as the inputs
– outputs ratio) (Giannoccaro, et al. 2008). .
According to Spleeman, et al. (2008) the comparison of volumetric pricing methods results
in reduced water use, increases in all the other inputs and reduced total gross margin.
Giannoccaro, et al. (2008) use a modified input – output method within which they
compare a volumetric pricing method with uniform rate, the area–based pricing method,
the output pricing method and the increased block-rate method. Their efficient ranking of
pricing methods were: volumetric pricing, input pricing, output pricing and the least
efficient was the area-based pricing.
6.2.3. The Modelling Framework
In order to assess the relative performance of various water pricing methods in our study
area, we used a combination of a biophysical agronomic and an economic model. The
adopted modelling framework is given by Figure3. It consists of two major components:
a) the biophysical section which is already presented in the previous section and b) the land
use model which is an optimization problem.
Standard meta-modelling procedures were applied to the data produced by the DNDC in
order to express algebraically the relations between yield, water and fertilizer. From the
analysis it was found that quadratic functions describe accurately the simulated crop
response to water and fertiliser use.
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Figure 3: The Modelling Framework
Overlay Maps
Agronomic
Data
Land Cover
Soil
De
m
Estimates of
environmental
impacts
Water
Budget
Meteorologic
al Data
DNDC
Metamodelin
g
(Simulation
Model)
Withwithout
Analysis
Land Use Model (Optimization)
Irrigatio
n Full
Cost
Simulated Results
Water Pricing
Scenarios
feedback
Data flow
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A brief structure of the land use model is as follows. For every cultivated crop i per soil
type j , the production function is described as:
yij  cij  aij1  Qijw  aij2  Qijf  aij3  Qijw  Qijf  aij4  (Qijw )2  aij5  (Qijf )2
(2)
where yij is the yield, in kg/ha, Qijw is water quantity in m3/ha, Qijf is nitrogen quantity,
in kg/ha, cij is the constant of the quadratic production function and aijk with k  1,...,5
are the variable coefficients.
The fertilizer cost function is given by the equation:
Ci f   P f  Qijf  lij
(3)
j
with Ci f denoting the fertilizer cost per crop in €, P f is nitrogen price in €/kg and lij is
the land area per crop, in ha. The water cost function per irrigated cultivated crop k i is
given as:
Ckwi   P w  lki j
(4)
j
where C kw denotes the water cost per irrigated crop in €, P w is the water price in €/ha.
i
Then, the total cost ( TC , in €) function is given by:


TC    Ci f  Ckwi  VEi   lij 
i 
j

(5)
where VEi stands for rest variable expenses expressed in €/ha. Total revenues ( TR , in €)
are:


TR    Pi   lij yij  Si   lij 
i 
j
j

(6)
where Pi denotes the product price in €/kg and Si crop subsidy, in €/ha. Then the
objective function of the problem is given by:
max TR  TC
(7)
Equation (7) represents a typical utilitarian measure of social welfare expressed by the total
gross margin for the study area. The objective function was maximised for all possible
water pricing scenarios under plausible agronomic constraints which were included to
match the actual and the simulated results.
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The main agronomic constraints included in the models are the following:
1) The subsidized cotton should produce at least 2000 kg/ha.
2) Inputs used are positive entities.
3) Crop evapotranspiration determine the upper bound of water applied per crop.
4) Upper bounds per soil type were derived by soil map described in the previous
section.
5) Drawing on Tsiros et al. (2009), land zoning were incorporated in the model. The
proportion of land characterised as unsustainable was devoted to compulsory setaside. As the data for the land of the study area indicate, it can be divided in two
zones per soil type.
6) It was assumed that the prevailing crop rotation schedule follows normative
prescriptions as identified by Karamanos (1999).
The model was solved by General Algebraic Modelling System (GAMS) using the Conopt3
solver1.
6.2.4. Water Pricing Scenarios
The water pricing scenarios examined are:
1) Land-based charging for recovering the irrigation full cost assessed in the previous
section. According to this each and every unit of irrigated land is charged the
amount
w
PLand

TC
 l
iki
where TC denotes the irrigation total costs estimated
ij
j
w
in the previous section. Note PLand
is independent of the volume of irrigation
w
water. Land-based charge of irrigation, PLand
, were found to be 179.2 €/ha and the
137.6 €/ha for the worst and best scenarios respectively.
2) Average total cost for recovering the irrigation full cost. According to this each and
w

every unit of irrigation water is charged the amount PATC
TC
. The
 Qijwlij
iki
1
j
The GAMS code for the optimization problem is given in the Appendix.
17 | P a g e
w
volumetric water price PATC
was estimated as 0.031 €/m3 and 0.024 €/m3 for the
worst and best scenarios respectively.
3) Two-part pricing method to jointly recover irrigation full cost and to achieve
efficiency. According to this, irrigation charges were based on a per area charge for
the recovery of environmental, resource and fixed financial cost and a per volume
charge of water used for the recovery of variable financial cost. So, each and every
unit of irrigated land is charged H 
FC  EC  SC
where FC denotes the
 lij
iki
j
LOLR fixed costs, EC is irrigation induced environmental costs and SC is
scarcity rent. At the same time, each and every unit of water consumption is
charged by the marginal price, MC , of water supply. Hence, the volumetric
component of the two-part pricing method is designed to achieves efficiency
whereas the non-volumetric component, H , is designed to recover the irrigation
full cost. The non-volumetric component was found to be 143.84 €/ha and 102.19
€/ha for the worst and best scenarios. The marginal cost was estimated by
regression analysis of cross-section data of fourteen Local Organizations of Land
Reclamation in Thessaly following Loehmann (2008). The estimated figure was
0.0146 €/m3.
6.2.5. Model Results
Table 2 summarizes the performance of the different water pricing methods according to
sound economic and environmental criteria. The main economic criterion refers to the
reduction of farmers’ welfare (total gross margin) brought about by different pricing
methods (see column TGM). By contrast, the main environmental assessment criteria refer
to the induced reductions in the water deficit (columns TWR and Deficit), and the
respective reduction in the pollution possibilities of irrigated land (columns TFR and IC).
To facilitate an overall comparison of alternatives judged by multiple criteria we construct
a simple measure to aggregate the ranking put forward individual criteria. This measure is:
TR  max  rij
(8)
j
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where rij is the rank of the ith pricing methods under the jth criterion. The last column in
Table 1 lists these overall rankings.
On the basis of the simulated results presented in Table 1, it is evident that the average
cost pricing dominates the two part tariff method since it achieves the highest score in the
total ranking. Such a result is valid under both worst (most expensive scenario) and best
scenario (cost effective). This is quite interesting result since the rationale for using an
average cost pricing is primarily based on cost recovery considerations. However, our
results indicate that outperforms two-part tariff method also on the basis environmental
criteria. The well-known inability of land-based water charges was also confirmed by the
findings of our simulations.
6.2.6. Conclusions
This study examined the relative performance of three possible water pricing methods,
namely the land-based charge, the average cost pricing and the two-part tariff charge. The
rationale for such a comparison is that a land-based charge is very often used in practise,
the average cost pricing is usually proposed when cost recovery is the main policy
objective, while the two-part tariff methods combines the rationales of cost recovery and
economic efficiency.
On the basis of our findings, the average cost pricing outperforms all other pricing
alternatives examined both on economic and environmental grounds. Such a result has
important policy implications since may guide water policy formation towards more
effective mater management regimes.
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Table 1: Results of different water pricing scenarios
Pollution
Implicit
Contributio
n (IC) of
irrigated
land
Water
Balance
(Deficit)
in mil
m3
%
change
of
Deficit
-39%
1
1.79
-5%
1
0.24
-16.9%
2
5.86
-5.6%
3
7
Averag
e Total
Cost -
11.37
-14%
3
43.38
-61%
3
1.69
-11%
2
0.18
-36.4%
3
1.19
-80.8%
1
14
Two –
part Tariff
10.08
-24%
1
43.44
-61%
2
1.42
-25%
3
0.27
-5.0%
1
3.51
-43.5%
2
9
Landbase -
10.79
-19%
2
66.35
-41%
1
1.68
-11%
3
0.25
-13.4%
1
5.90
-4.9%
1
8
Averag
e Total
Cost -
11.07
-17%
3
43.38
-61%
3
1.69
-11%
2
0.18
-36.4%
3
1.19
-80.8%
3
14
Two –
part Tariff
9.89
-25%
1
57.78
-48%
2
1.74
-8%
1
0.23
-18.4%
2
3.23
-48.0%
2
8
1.89
%
change
of IC
Rank
67.89
112.13
Rank
2
13.27
Rank
-15%
Rank
Total Rank
%
chang
e of
TFR
Rank
Total
Fertilizer
Requirements
(TFR) in
mil kg
11.32
Baseline
Best
Total Water
%
Requirement change
s (TWR) in
of
mil m3
TWR
Landbase
Scenario
Worst
Total
Gross
%
Margin chang
(TGM
e of
) in
TGM
mil €
0.29
6.20
20
21
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26
Appendix : Gams code for the Land Use Model
$ontext
Maximization of farmers' Profit.
The total area of the Regional Organization of Land Reclamation of Pinios is
considered as one single optimizing unit.
$offtext
* +++++++++++++++ *
* Declare Indexes *
* +++++++++++++++ *
Set
crop type of cultivated crops /cotton1, cotton2, cotton3, maize1, maize2, wheat,
barley, alfalfa, sorghum, compulsorysetaside/
soil type of soil /clay, clayloam, siltyloam/
;
set
subcrop1(crop) all crops except cotton3(cotton activity);
subcrop1(crop) = yes; subcrop1("cotton3") = no;
display subcrop1;
set
subcrop2(crop) all crops except compulsory setaside;
subcrop2(crop) = yes; subcrop2("compulsorysetaside") = no;
display subcrop2;
set
subcrop3(crop) all crops except cotton3 and compulsory setaside;
subcrop3(crop) = yes; subcrop3("cotton3") = no; subcrop3("compulsorysetaside") = no;
display subcrop3;
set
irrigatedcrop(crop) irrigated crops;
irrigatedcrop(crop) = yes; irrigatedcrop("wheat") = no; irrigatedcrop("barley") = no;
irrigatedcrop("compulsorysetaside") = no;
display irrigatedcrop;
set
dripirrigated(crop) drip irrigated crops;
dripirrigated(crop) = no; dripirrigated("cotton1") = yes; dripirrigated("maize1") =
yes;
display dripirrigated;
set
gunirrigated(crop) gun irrigated crops;
gunirrigated(crop) = no; gunirrigated("cotton2") = yes; gunirrigated("maize2") = yes;
gunirrigated("alfalfa") = yes; gunirrigated("sorghum") = yes;
display gunirrigated;
* ++++++++++++++++++ *
* Declare Parameters *
* ++++++++++++++++++ *
Parameters
cropprice(crop) price of yield per crop type in euros per kg
/wheat 0.18, barley 0.14, cotton1 0.50, cotton2 0.50, cotton3 0.50,
maize1 0.15, maize2 0.15, alfalfa 0.15, sorghum 0.03, compulsorysetaside 0/
VariableExpenses(crop) the variable expenses of cultivated crops in euros per hectare
/cotton1 927.40, cotton2 647.40, cotton3 647.40, maize1 1055.14, maize2
775.14,
wheat 207.24, barley 152.07, alfalfa 736.47, sorghum 555.87,
compulsorysetaside 0/
subsidy(crop) the subsidy of cultivated crops in euros per hectare
/cotton1 0, cotton2 0, cotton3 805.6, maize1 0, maize2 0,
wheat 85.8, barley 0, alfalfa 0, sorghum 0, compulsorysetaside 454.25/
fertilizercottonactivity(soil) fertilizer for cotton cultivated for subsidy in kg per
hectare
/clay 51.055, clayloam 20.8, siltyloam 19.27/
zone1(soil) Sustainable-Intermediate production zone per soil type in ha
/clay 5705.6, clayloam 8237.5, siltyloam 3591/
zone2(soil) Unsustainable production zone per soil type in ha
/clay 572.7, clayloam 826.9, siltyloam 360.5/
;
table
yield_c(crop, soil) constant of quadratic yield function per crop and soil
type
clay
clayloam
siltyloam
wheat
1924.1034
1480.1891
1965.8353
barley
835.5505
1467.7718
1588.0882
cotton1
1214.8099
1988.8332
2007.6947
cotton2
1214.8099
1988.8332
2007.6947
maize1
3825.9910
5348.5764
5203.7372
maize2
3825.9910
5348.5764
5203.7372
alfalfa
6936.1000
7144.8000
7391.5000
27
sorghum
3752.7829
6021.5783
6452.3280
;
table
yield_a1(crop, soil) a1 coefficient (for irrigation) of quadratic yield
function per crop and soil type
clay
clayloam
siltyloam
wheat
0
0
0
barley
0
0
0
cotton1
0.1248
0.1348
0.1320
cotton2
0.1248
0.1348
0.1320
maize1
0.7550
0.9249
1.1230
maize2
0.7550
0.9249
1.1230
alfalfa
0.8290
0.8290
0.8290
sorghum
-0.2650
-0.8295
-0.8900
;
table
yield_a2(crop, soil) a2 coefficient (for fertilizer) of quadratic yield
function per crop and soil type
clay
clayloam
siltyloam
wheat
16.5064
21.3132
15.9892
barley
10.4429
8.1678
8.9499
cotton1
19.3877
17.0072
15.5660
cotton2
19.3877
17.0072
15.5660
maize1
56.1851
46.7851
43.9828
maize2
56.1851
46.7851
43.9828
alfalfa
31.3000
31.3000
31.3000
sorghum
31.9727
30.0343
30.4571
;
table
yield_a3(crop, soil) a3 coefficient (for fertilizer-irrigation interaction) of
quadratic yield function per crop and soil type
clay
clayloam
siltyloam
wheat
0
0
0
barley
0
0
0
cotton1
0.0019
0.0020
0.0021
cotton2
0.0019
0.0020
0.0021
maize1
0.0060
0.0057
0.0057
maize2
0.0060
0.0057
0.0057
alfalfa
0.0020
0.0020
0.0020
sorghum
0.0003
0.0003
0.0003
;
table
yield_a4(crop, soil) a4 coefficient (for squared irrigation) of quadratic
yield function per crop and soil type
clay
clayloam
siltyloam
wheat
0
0
0
barley
0
0
0
cotton1
-0.0000372
-0.0000377
-0.0000363
cotton2
-0.0000372
-0.0000377
-0.0000363
maize1
-0.0001
-0.0001
-0.0001
maize2
-0.0001
-0.0001
-0.0001
alfalfa
-0.0001
-0.0001
-0.0001
sorghum
0.000036
0.000100
0.000100
;
table
yield_a5(crop, soil) a5 coefficient (for squared fertilizer) of quadratic
yield function per crop and soil type
clay
clayloam
siltyloam
wheat
-0.0244
-0.0353
-0.0135
barley
0.0157
0.0410
0.0362
cotton1
-0.0767
-0.1118
-0.1078
cotton2
-0.0767
-0.1118
-0.1078
maize1
-0.1530
-0.1522
-0.1549
maize2
-0.1530
-0.1522
-0.1549
alfalfa
-0.0220
-0.0220
-0.0220
sorghum
-0.0128
-0.0104
-0.0091
;
scalar
waterprice_baseline the price of water for the baseline scenario in euros per hectare
/89.11/
fertilizerprice the price of fertilizer(nitrogen) in euros per kg /1.76/
landstock total available land of study area in hectares /19294.2/
clayland total available clay land of study area in hectares /6278.33/
clayloamland total available clayloam land of study area in hectares /9064.42/
siltyloamland total available siltyloam land of study area in hectares /3951.45/
irrigatedland available irrigated land of study area in hectares /17074.5/
alfastock the total land covered with alfalfa in baseline scenario in hectares
/1420.38/
dripefficiency the efficiency of drip irrigation system /0.9/
gunefficiency the efficiency of gun irrigation system /0.7/
yieldcottonactivity yield of cotton cultivated for subsidy in kg per hectare /2000/
28
watercottonactivity water required for cotton cultivated for subsidy in m3 per hectare
/5983/
;
* +++++++++++++++++++++++++++++ *
*
Model Variables
*
* +++++++++++++++++++++++++++++ *
Positive Variable
land_var(crop,soil) the land devoted per crop and soil type in hectares
landconstraint_var the constraint for the total land in hectares
totalclayland_var the total clay land in hectares
totalclayloamland_var the total clayloam land in hectares
totalsiltyloamland_var the total siltyloam land in hectares
yieldperhectare_var(crop,soil) the yield in kg per hectare of cultivated crop and soil
type
yield_var(crop,soil) the total yield in kg per cultivated subcrop1 and soil type
yieldcotact_var(soil) the total yield of cotton activity in kg per cultivated soil
type
waterrequirement_var(crop,soil) water requirements in m3 per hectare
twr_drip_var(crop,soil) total water requirements for drip irrigated crops in m3 per
cultivated land
twr_gun_var(crop,soil) total water requirements for gun irrigated crops in m3 per
cultivated land
twr_cotact_var(soil) total water requirements for cotton activity in m3 per cultivated
land
grandtotalwaterreq_var water requirements for the study area in m3
waterapplied1_var(crop,soil) water applied to drip irrigated crops in m3 per hectare
waterapplied2_var(crop,soil) water applied to gun irrigated crops in m3 per hectare
fertilizerrequirement_var(crop,soil) fertilizer(nitrogen) requirements in kg per
hectare
tfr_subcrop3_var(crop,soil) total fertilizer requirements for subcrops3 in kg per
cultivated land
tfr_cotact_var(soil) total fertilizer requirements for cotton3 in kg per cultivated
land
grandtotalfertilizerreq_var total fertilizer requirements for the study area in kg
fertilizercost_var(crop) the cost of fertilizer in euros per cultivated subcrop1
fercost_cotact_var the cost of fertilizer in euros for cotton activity
irrigationcost_baseline_var(crop) the cost of irrigation in euros per cultivated crop
for the baseline scenario
VariableExpenses_var(crop) the variable expenses in euros per cultivated crop
totalcost_subcrop3_var(crop) the total cost in euros per cultivated subcrop3
totalcost_cotact_var the total cost in euros for cotton activity
grandtotalcost_var the grand total cost in euros of the study area
revenue_subcrop3_var(crop) the revenue in euros per cultivated subcrop3
revenue_cotact_var the revenue in euros for cotton activity
subsidy_var(crop) the subsidy in euros per cultivated crop
totalrevenue_subcrop1_var(crop) the total revenue in euros per cultivated subcrop1
totalrevenue_cotact_var the total revenue in euros for cotton activity
grandtotalrevenue_var the grand total revenue in euros of the study area
gm_subcrop1_var(crop) the gross margin per subcrop1 in euros
gm_cotact_var the gross margin for cotton activity in euros
alfalfarotation_var
setasideland_var the total land of setaside in hectares
land_cotton the total land covered with cotton in hectares (cotton1 plus cotton2 plus
cotton3)
land_maize the total land covered with maize in hectares
;
Variables
GrossMargin_var the Gross Margin of the study area in euros(*10^7)
;
* +++++++++++++++++++++++++++++ *
*
Bounds for Variables
*
* +++++++++++++++++++++++++++++ *
yieldperhectare_var.lo(crop,soil) = 0.0000000000000000000000000000000000000001;
yieldperhectare_var.up("maize1",soil) = 11000;
yieldperhectare_var.up("maize2",soil) = 11000;
yieldperhectare_var.lo("cotton1",soil) = 0;
yieldperhectare_var.lo("cotton2",soil) = 0;
fertilizerrequirement_var.lo(crop,soil) = 0;
waterrequirement_var.lo(crop,soil) = 0;
*the upper and lower bounds of water requirements are equal to the real water
requirements(ET) plus/minus 20%
waterrequirement_var.up("wheat",soil) = 0;
waterrequirement_var.up("barley",soil) = 0;
waterrequirement_var.lo("cotton1",soil) = 100;
waterrequirement_var.up("cotton1",soil) = 7000;
29
waterrequirement_var.lo("cotton2",soil) = 100;
waterrequirement_var.up("cotton2",soil) = 7000;
waterrequirement_var.lo("maize1",soil) = 100;
waterrequirement_var.up("maize1",soil) = 6000;
waterrequirement_var.lo("maize2",soil) = 100;
waterrequirement_var.up("maize2",soil) = 6000;
waterrequirement_var.lo("alfalfa",soil) = 100;
waterrequirement_var.up("alfalfa",soil) = 9000;
waterrequirement_var.up("sorghum",soil) = 3000;
fertilizerrequirement_var.lo("cotton1",soil) = 50;
fertilizerrequirement_var.up("cotton1",soil) = 160;
fertilizerrequirement_var.lo("cotton2",soil) = 50;
fertilizerrequirement_var.up("cotton2",soil) = 160;
fertilizerrequirement_var.lo("maize1",soil) = 60;
fertilizerrequirement_var.up("maize1",soil) = 200;
fertilizerrequirement_var.lo("maize2",soil) = 60;
fertilizerrequirement_var.up("maize2",soil) = 200;
fertilizerrequirement_var.lo("alfalfa",soil) = 20;
fertilizerrequirement_var.up("alfalfa",soil) = 50;
fertilizerrequirement_var.lo("sorghum",soil) = 0;
fertilizerrequirement_var.up("sorghum",soil) = 200;
fertilizerrequirement_var.lo("wheat",soil) = 0;
fertilizerrequirement_var.up("wheat",soil) = 150;
fertilizerrequirement_var.lo("barley",soil) = 0;
fertilizerrequirement_var.up("barley",soil) = 150;
landconstraint_var.up = landstock;
totalclayland_var.up = clayland;
totalclayloamland_var.up = clayloamland;
totalsiltyloamland_var.up = siltyloamland;
*Hypothesis: the alfalfa land can be increased at x% of the baseline scenario
alfalfarotation_var.up = alfastock;
* +++++++++++++++++++++++++++++ *
*
Model Equations
*
* +++++++++++++++++++++++++++++ *
Equations
objectivefunction the GrossMargin in euros
yieldperhectare_equ(crop,soil)
yield_equ(crop,soil)
yieldcotact_equ(soil)
waterapplied1_equ(crop,soil)
waterapplied2_equ(crop,soil)
twr_drip_equ(crop,soil)
twr_gun_equ(crop,soil)
twr_cotact_equ(soil)
grandtotalwaterreq_equ
tfr_subcrop3_equ(crop,soil)
tfr_cotact_equ(soil)
grandtotalfertilizerreq_equ
fertilizercost_equ(crop)
fercost_cotact_equ
irrigationcost_baseline_equ(crop)
VariableExpenses_equ(crop)
totalcost_subcrop3_equ(crop)
totalcost_cotact_equ
grandtotalcost_equ
revenue_subcrop3_equ(crop)
revenue_cotact_equ
subsidy_equ(crop)
totalrevenue_subcrop1_equ(crop)
totalrevenue_cotact_equ
grandtotalrevenue_equ
gm_subcrop1_equ(crop)
gm_cotact_equ
landconstraint_equ
zone1_equ(soil)
totalclayland_equ
totalclayloamland_equ
totalsiltyloamland_equ
alfalfarotation_equ
setaside1_equ
setaside2_equ(soil)
aggregation1_equ
aggregation2_equ
rotationlimit1_equ
rotationlimit2_equ
rotationlimit3_equ
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rotationlimit4_equ
*rainfed_equ
;
* +++++++++++++++++++++++++++++ *
*
Definition of Equations
*
* +++++++++++++++++++++++++++++ *
******************************************************************YIELD***************
***********************************************
*Yield Equation for every type of crop and soil per cultivated hectare
yieldperhectare_equ(crop,soil)$subcrop3(crop)..
yieldperhectare_var(crop,soil)
=e=
yield_c(crop,soil)
+ yield_a1(crop,soil)*waterrequirement_var(crop,soil)
+
yield_a2(crop,soil)*fertilizerrequirement_var(crop,soil)
+
yield_a3(crop,soil)*waterrequirement_var(crop,soil)*fertilizerrequirement_var(crop,soi
l)
+
yield_a4(crop,soil)*waterrequirement_var(crop,soil)**2
+
yield_a5(crop,soil)*fertilizerrequirement_var(crop,soil)**2;
*Yield Equation for every type of crop and soil per cultivatd land
yield_equ(crop,soil)$subcrop3(crop)..
yield_var(crop,soil) =e=
yieldperhectare_var(crop,soil)*land_var(crop,soil);
yieldcotact_equ(soil)..
yieldcotact_var(soil) =e=
yieldcottonactivity*land_var("cotton3",soil);
*******************************************************************WATER**************
*************************************************
*Quantity of water that must be applied in order to cover the crop requirements due to
field efficiency
waterapplied1_equ(crop,soil)$(dripirrigated(crop))..
waterapplied1_var(crop,soil) =e=
waterrequirement_var(crop,soil)/dripefficiency;
waterapplied2_equ(crop,soil)$(gunirrigated(crop))..
waterapplied2_var(crop,soil) =e=
waterrequirement_var(crop,soil)/gunefficiency;
*The water requirements per crop and soil type
twr_drip_equ(crop,soil)$(dripirrigated(crop)).. twr_drip_var(crop,soil) =e=
waterapplied1_var(crop,soil)*land_var(crop,soil);
twr_gun_equ(crop,soil)$(gunirrigated(crop)).. twr_gun_var(crop,soil) =e=
waterapplied2_var(crop,soil)*land_var(crop,soil);
twr_cotact_equ(soil).. twr_cotact_var(soil) =e=
(watercottonactivity/gunefficiency)*land_var("cotton3",soil);
grandtotalwaterreq_equ.. grandtotalwaterreq_var =e= sum ((crop,soil),
twr_drip_var(crop,soil))
+ sum ((crop,soil),
twr_gun_var(crop,soil))
+ sum (soil,twr_cotact_var(soil));
*The cost of irrigation per cultivated crop (area-based pricing)
irrigationcost_baseline_equ(crop)$(irrigatedcrop(crop))..
irrigationcost_baseline_var(crop) =e=
waterprice_baseline*(sum(soil,land_var(crop,soil)));
*****************************************************************FERTILIZER***********
***************************************************
*The fertilizer requirements per crop and soil type
tfr_subcrop3_equ(crop,soil)$subcrop3(crop).. tfr_subcrop3_var(crop,soil) =e=
fertilizerrequirement_var(crop,soil)*land_var(crop,soil);
tfr_cotact_equ(soil).. tfr_cotact_var(soil) =e=
fertilizercottonactivity(soil)*land_var("cotton3",soil);
grandtotalfertilizerreq_equ.. grandtotalfertilizerreq_var =e= sum ((crop,soil),
tfr_subcrop3_var(crop,soil))
+ sum
(soil,tfr_cotact_var(soil));
*The cost of fertilizer per cultivated crop
fertilizercost_equ(crop)$subcrop3(crop).. fertilizercost_var(crop) =e=
fertilizerprice*(sum(soil,tfr_subcrop3_var(crop,soil)$subcrop3(crop)));
fercost_cotact_equ.. fercost_cotact_var =e=
fertilizerprice*(sum(soil,tfr_cotact_var(soil)));
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****************************************************************COST
ESTIMATION************************************************************
*The Variable Expenses per cultivated crop
VariableExpenses_equ(crop).. VariableExpenses_var(crop) =e= VariableExpenses(crop) *
sum ((soil),land_var(crop,soil));
*The total cost per subcrop1 equals the summation of fertilizer costs plus irrigation
costs plus variable expenses
totalcost_subcrop3_equ(crop)$subcrop3(crop).. totalcost_subcrop3_var(crop) =e=
fertilizercost_var(crop) + irrigationcost_baseline_var(crop)
+ VariableExpenses_var(crop);
*The total cost for cotton activity
totalcost_cotact_equ.. totalcost_cotact_var =e=
fercost_cotact_var + irrigationcost_baseline_var("cotton3") +
VariableExpenses_var("cotton3");
*The grand total cost equals the summation of total costs
grandtotalcost_equ.. grandtotalcost_var =e= totalcost_cotact_var + sum(crop,
totalcost_subcrop3_var(crop)$subcrop3(crop));
***************************************************************REVENUE
ESTIMATION*********************************************************
*The revenue per cultivated crop
revenue_subcrop3_equ(crop)$subcrop3(crop).. revenue_subcrop3_var(crop) =e=
cropprice(crop)*sum(soil, yield_var(crop,soil));
revenue_cotact_equ.. revenue_cotact_var =e= cropprice("cotton3")*sum(soil,
yieldcotact_var(soil));
*The subsidy per cultivated crop
subsidy_equ(crop).. subsidy_var(crop) =e= subsidy(crop)*sum((soil),
land_var(crop,soil));
*The total revenue per subcrop1 equals the summation of revenue plus subsidy
totalrevenue_subcrop1_equ(crop)$subcrop1(crop).. totalrevenue_subcrop1_var(crop) =e=
revenue_subcrop3_var(crop)$subcrop3(crop) + subsidy_var(crop);
*The total revenue for cotton activity
totalrevenue_cotact_equ.. totalrevenue_cotact_var =e= revenue_cotact_var +
subsidy_var("cotton3");
*The grand total revenue equals the summation of total revenues
grandtotalrevenue_equ.. grandtotalrevenue_var =e= totalrevenue_cotact_var + sum(crop,
totalrevenue_subcrop1_var(crop)$subcrop1(crop));
*************************************************************GROSS MARGIN
ESTIMATION*******************************************************
*The Gross Margin per subcrop1
gm_subcrop1_equ(crop).. gm_subcrop1_var(crop)$subcrop1(crop) =e=
totalrevenue_subcrop1_var(crop)$subcrop1(crop) totalcost_subcrop3_var(crop)$subcrop3(crop);
*The Gross Margin for cotton activity
gm_cotact_equ.. gm_cotact_var =e= totalrevenue_cotact_var - totalcost_cotact_var;
*The objective function: Estimation of the Gross Margin as the difference between the
grand total revenue and the grand total cost
objectivefunction.. GrossMargin_var =e= (grandtotalrevenue_var grandtotalcost_var)/10000000;
* +++++++++++++++++++++++++++++ *
*
Land management
*
* +++++++++++++++++++++++++++++ *
landconstraint_equ.. landconstraint_var =e= sum ((crop,soil), land_var(crop,soil));
zone1_equ(soil)..
sum(crop, land_var(crop,soil)$subcrop2(crop))=l=zone1(soil);
totalclayland_equ.. totalclayland_var =g= sum(crop,
land_var(crop,"clay")$subcrop2(crop))+zone2("clay");
totalclayloamland_equ.. totalclayloamland_var =g= sum(crop,
land_var(crop,"clayloam")$subcrop2(crop))+zone2("clayloam");
totalsiltyloamland_equ.. totalsiltyloamland_var =g= sum(crop,
land_var(crop,"siltyloam")$subcrop2(crop))+zone2("siltyloam");
setaside1_equ.. setasideland_var =e= sum(soil, land_var("compulsorysetaside",soil));
setaside2_equ(soil).. land_var("compulsorysetaside",soil) =e= zone2(soil);
*Hypothesis: the alfalfa land can be increased at x% of the baseline scenario - see
bounds
alfalfarotation_equ.. alfalfarotation_var =e= sum(soil,land_var("alfalfa",soil));
*ROTATION: cotton - maize - wheat - sorghum - alfalfa - barley
aggregation1_equ..
land_cotton =e= sum(soil,
land_var("cotton1",soil)+land_var("cotton2",soil)+land_var("cotton3",soil));
aggregation2_equ..
land_maize =e= sum(soil,
land_var("maize1",soil)+land_var("maize2",soil));
rotationlimit1_equ..
land_cotton =l= 14559;
rotationlimit2_equ..
sum(soil,land_var("wheat",soil)) =l= 4*land_maize;
rotationlimit3_equ.. land_maize =l= 400;
rotationlimit4_equ.. - (3/5)*sum (soil, land_var("alfalfa",soil))
+ (2/5)*(land_maize+ land_cotton+sum(soil,
land_var("wheat",soil)+land_var("barley",soil)+land_var("sorghum",soil))) =g= 0;
*rainfed_equ..
sum(soil,land_var("wheat",soil))+sum(soil,land_var("barley",soil))=l=1650;
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**************************************************************************************
**********************************************
model GrossMargin /all/;
option limrow = 0;
option limcol = 0;
option nlp = conopt3;
GrossMargin.scaleopt = 1;
Solve GrossMargin using nlp maximizing GrossMargin_var;
display GrossMargin_var.l;
display gm_subcrop1_var.l;
display gm_cotact_var.l;
display grandtotalrevenue_var.l;
display totalrevenue_subcrop1_var.l;
display totalrevenue_cotact_var.l;
display revenue_subcrop3_var.l;
display revenue_cotact_var.l;
display subsidy_var.l;
display grandtotalcost_var.l;
display totalcost_subcrop3_var.l;
display totalcost_cotact_var.l;
display VariableExpenses_var.l;
display fertilizercost_var.l;
display fercost_cotact_var.l;
display irrigationcost_baseline_var.l;
display land_var.l;
display land_cotton.l;
display land_maize.l;
display landconstraint_var.l;
display yieldperhectare_var.l;
display yield_var.l;
display waterrequirement_var.l;
display waterapplied1_var.l;
display waterapplied2_var.l;
display twr_drip_var.l;
display twr_gun_var.l;
display twr_cotact_var.l;
display grandtotalwaterreq_var.l;
display fertilizerrequirement_var.l;
display tfr_subcrop3_var.l;
display tfr_cotact_var.l;
display grandtotalfertilizerreq_var.l;
display setasideland_var.l;
display totalclayland_var.l;
display totalclayloamland_var.l;
display totalsiltyloamland_var.l;
display alfalfarotation_var.l;
parameter
output1(*,crop,soil) results from model runs for different crops and soils
output2(*,crop) results from model runs for different crops
output3(*,soil) results from model runs for cotton activity in different soils
;
output1("land",crop,soil) = land_var.l(crop,soil);
output1("yield per hectare",crop,soil) = yieldperhectare_var.l(crop,soil);
output1("yield",crop,soil) = yield_var.l(crop,soil);
output1("water quantity",crop,soil) = waterrequirement_var.l(crop,soil);
output1("water applied dripirrigted",crop,soil) = waterapplied1_var.l(crop,soil);
output1("water applied gunirrigted",crop,soil) = waterapplied2_var.l(crop,soil);
output1("total watreq drip",crop,soil) = twr_drip_var.l(crop,soil);
output1("total watreq gun",crop,soil) = twr_gun_var.l(crop,soil);
output1("fertilizer quantity",crop,soil) = fertilizerrequirement_var.l(crop,soil);
output1("total fertilizer subcrop3",crop,soil) = tfr_subcrop3_var.l(crop,soil);
output2("fertilizer cost per subcrop3",crop) = fertilizercost_var.l(crop);
output2("irrigation cost baseline",crop) = irrigationcost_baseline_var.l(crop);
output2("variable expenses", crop) = VariableExpenses_var.l(crop);
output2("total cost per subcrop3",crop) = totalcost_subcrop3_var.l(crop);
output2("revenue per subcrop3",crop) = revenue_subcrop3_var.l(crop);
output2("subsidy",crop) = subsidy_var.l(crop);
output2("total revenue per subcrop1",crop) = totalrevenue_subcrop1_var.l(crop);
output2("gross margin per subcrop1",crop) = gm_subcrop1_var.l(crop);
output3("total yield cot_act",soil) = yieldcotact_var.l(soil);
output3("total watreq cot_act",soil) = twr_cotact_var.l(soil);
output3("total ferteq cot_act",soil) = tfr_cotact_var.l(soil);
option output1:2:1:2;
option output2:2:1:1;
option output3:2:1:1;
display output1;
display output2;
33
display output3;
*execute_Unload "modeloutput1_Baseline.gdx" output1;
*execute "Gdxxrw modeloutput1_Baseline.gdx o=modeloutput1_Baseline.xls par=output1
Rng=sheet1!a1 rdim=1 cdim=2 ";
*execute_Unload "modeloutput2_Baseline.gdx" output2;
*execute "Gdxxrw modeloutput2_Baseline.gdx o=modeloutput2_Baseline.xls par=output2
Rng=sheet1!a1 rdim=1 cdim=1 ";
*execute_Unload "modeloutput3_Baseline.gdx" output3;
*execute "Gdxxrw modeloutput3_Baseline.gdx o=modeloutput3_Baseline.xls par=output3
Rng=sheet1!a1 rdim=1 cdim=1 ";
34