Agricultural Water Management 153 (2015) 20–31
Contents lists available at ScienceDirect
Agricultural Water Management
journal homepage: www.elsevier.com/locate/agwat
Shadow price of water for irrigation—A case of the High Plains
Jadwiga R. Ziolkowska ∗
The University of Oklahoma, Department of Geography and Environmental Sustainability, 100 East Boyd St., Norman, OK 73019 United States
a r t i c l e
i n f o
Article history:
Received 26 March 2014
Accepted 28 January 2015
Available online 26 February 2015
Keywords:
Water
Irrigation
Residual valuation
Shadow price
High Plains
US
a b s t r a c t
The 2011 and 2012 droughts considerably affected the Ogallala Aquifer supplying irrigation water for
agricultural production in the US High Plains (HP). Shrinking water resources and growing demand for
water create a challenging tradeoff situation. This also poses a question about the value of water and
efficient water allocation. Currently, water rates for irrigating crops paid by farmers do not reflect the
actual value of water that can be expressed solely as a shadow price. Also studies are missing that would
comprehensively compare different states and different crops in one methodological framework. This
paper helps to fill this gap. Farm-budget residual valuation is applied to estimate the shadow price of
water for irrigation in three High Plains states: Texas, Kansas and Nebraska, for five prevailing crops:
corn, cotton, sorghum, soybean, and wheat.
Among the analyzed High Plains states the highest shadow price of water was found for wheat production in the Texas Northern High Plains ($865.99/af = $0.70/m3 ), while the lowest shadow price was
found for corn in the Texas Southern High Plains ($5.13/af = $0.004/m3 ). The study can be helpful to stakeholders and policy makers to evaluate scenarios and tradeoffs between profitable crop production and
conservation of water resources.
© 2015 Elsevier B.V. All rights reserved.
1. Introduction
According to the most recent (2005) survey by USGS (2013a), in
the last 60 years, the total water withdrawals for irrigation in the US
showed an increasing trend between 1950 and 1980, and reached
the peak in 1980 at the level of 150 billion gallons per day (Bgal/d)
(567.8 million m3 ). Since then, water withdrawals for irrigation
in the US have been decreasing and dropped to 128 Bgal/d (484.5
million m3 ) in 2005. Water withdrawals for the entire agricultural
sector in the US amounted to 139 Bgal/d (50,826.7 Bgal/year) (192.4
billion m3 /year) in 2005, which accounts for 40.2% of the total
water withdrawals in the country (author’s calculations based on
UN Water (2013)). Irrigation accounts for more than 90% of the agricultural water use and represents the largest single consumptive
water use in the US (US Environmental Protection Agency (EPA),
2012).
The above mentioned developments pose a question about economics of water resources in the agricultural sector. This paper
addresses this question based on the example of the US High Plains
(HP). The research question is relevant as the agricultural sector
plays an important role in the High Plains with 28% of the agricultural land being irrigated (USGS, 2013b; US EPA, 2007). The research
∗ Tel.: +1 405 325 9862; fax: +1 405 325 6090.
E-mail address: [email protected]
http://dx.doi.org/10.1016/j.agwat.2015.01.024
0378-3774/© 2015 Elsevier B.V. All rights reserved.
presented in this paper is also timely as the High Plains region has
been plagued by extreme drought for the past several years, which
resulted in an increasing pressure on water resources.
Currently, knowledge about the actual value of water as a
resource is very limited. While the water rates represent the costs
of extracting water from aquifers and delivering it to the final consumer, they do not reflect the real value of the resource. Thus, water
for irrigating crops is underpriced. This can lead to an irreversible
depletion of the Ogallala Aquifer in the mid-term and can inevitably
stymie agricultural production in the High Plains region. In order
to avoid such scenarios from happening in the near future, it is crucial to estimate the actual value of water for irrigation in the High
Plains and assess the water rates that would allow farmers to still
breakeven, while also protecting water resources at the same time.
This paper seeks to answer this question for three selected states
in the High Plains: Texas, Kansas and Nebraska.
The contribution presented in this paper is novel in that the
value of water for irrigation has not been previously analyzed
with a comparative and comprehensive analysis for different states
and different crops in the High Plains region. Previous studies
in this field were focused on single regions in the High Plains,
and in addition they applied various methodologies (e.g., farm
budget analysis, change in net income (CINI) method or programming methods). Thus, a direct comparison of the value of water
for irrigation among different states in the High Plains was not
possible. By using one methodological approach for each of the
J.R. Ziolkowska / Agricultural Water Management 153 (2015) 20–31
analyzed states, this paper provides a comparative-static analysis that can be used for further regional and large-scale program
planning analyses.
The actual value of water for irrigation has been expressed in this
paper as the shadow price of water. The shadow price has many
definitions in the literature. Here, the shadow price of water for
irrigation was methodologically defined and calculated as the ratio
between the production net returns and the total amount of water
used for irrigating the respective crops. Conceptually, the shadow
price of water can also be viewed as the difference between the
given water rate for irrigation and the actual economic value of
water as a natural resource. In other words, the shadow price estimated here reflects the price that would need to be paid by farmers
to veritably account for the actual value of water. Due to the applied
methodology (residual valuation), the shadow price of water can
also be referred to as residual value of water. In order to maintain
the separation between the theoretical concept and the methodology applied in this paper, the term ‘shadow price of water’ will be
used throughout the paper.
This paper applies farm-budget residual valuation due to its
simplicity and robustness. An extensive review of the residual valuation methodology has been provided by Young (2005a,b). Despite
the relevance of evaluating the shadow price of water, the number of studies applying the residual valuation method is rather
limited (Hellegers and Davidson, 2010). Berbel et al. (2011) applied
the method to determine an aggregate value for agricultural water
use across regions in Spain. Also Hellegers and Davidson (2010)
used residual valuation to determine the disaggregated economic
value of irrigation water used in agriculture across crops, zones and
seasons in the Musi sub-basin in India. Otherwise, recent studies
applying this methodology are missing.
This paper has two goals: (1) it presents a practical application
of the residual valuation method for the High Plains region, and (2)
it extends the standard methodological proceeding by considering
differences occurring between regions and different crops.
The results of the study can be used by policy makers and
stakeholders to evaluate scenarios and tradeoffs between profitable agricultural production in the region and a sustainable level of
water protection and conservation.
The paper is structured as follows. Section 2 depicts the concept
of economic value of water and economic approaches to measure it.
Section 3 presents the case study region—the US High Plains in the
context of crop production conditions. In Section 4, methodology
and data are presented with state specific assumptions. Section 5
presents results and a discussion on the shadow price in the analyzed regions as well as a comparison analysis for the Texas High
Plains in 2010 and 2011. Section 6 discusses limitations and challenges of the residual valuation methodology. Finally, conclusions
and outlook are presented in Section 7.
21
management of water, particularly in less-developed countries by
establishing rational market-based institutions to solve problems
of water availability, quality, and access (Euzen and Morehouse,
2011). Also, US EPA (2013) underlined that water is not a onedimensional commodity and the user’s willingness to pay for water
from a particular source may depend on water quantity, quality,
time, space, and access reliability.
According to US EPA (2013), the future economic value of
water will rise, driven by the competition in water allocation
between different sectors. This will create even a greater need for
decision-makers in the private and public sectors for additional
information that can help them maximize the benefits derived
from water use. Existing estimates of the economic value of water
are relatively few and vary significantly within and across sectors. In 2012, the economic value of water was estimated to
amount to $12–$4500/acre-foot (af) ($0.01–3.65/m3 ) in the agricultural sector, $14–$1600/af ($0.01–1.30/m3 ) in manufacturing,
$12–$87/af ($0.01–0.07/m3 ) for cooling water at thermoelectric
power plants, $1–$157/af (0.00–0.13/m3 ) for hydropower generation, $40–$2700/af ($0.03–2.19/m3 ) for mining and energy
resource extraction, and up to $4500/af ($3.65/m3 ) for public supply and domestic self-supply (US Environmental Protection Agency
(EPA), 2012). Several studies evaluated the economic value of
irrigation water in the European Union as well as water policies for
irrigated agriculture (Gomez-Limon et al., 2002; Gomez-Limon and
Riesgo, 2004; Gomez-Limon, 2004). A study by Rigby et al. (2010)
for Spain found marginal water values to be typically above those
paid by farmers.
Several previous studies analyzed various aspects of optimal
irrigation strategies and the economic value of water in the High
Plains. For example, according to Schloss et al. (2000) and the
Kansas Geological Survey, reductions in authorized water use of
at least 75% are needed in many areas of Western Kansas for water
use to meet criteria of a sustainable yield. Lilienfeld and Asmild
(2007) applied Data Envelopment Analysis to identify farms with
the highest irrigation efficiency in Kansas, based on the reduction
potential or excess of irrigation water. This paper builds up on the
past research in the field and extends the analysis by evaluating the
economic value of water in different states of the High Plains and
for different crops.
In recent years, most studies have focused on estimating the
economic value of irrigation by comparing irrigated versus nonirrigated agricultural production. Only a few recent studies focus
directly on the shadow price of water for irrigation (Mesa-Jurado
et al., 2012; Hellegers and Davidson, 2010). This paper seeks to fill
this theoretical and methodological gap and extend the literature
in this field.
3. Case study area—High Plains
2. Economic value of water for irrigation—Concept and
evaluation approaches
The concept of value of water has been adopted as one of the
principles at the 1992 International Conference on Water and the
Environment in Dublin that indicated that ‘water has an economic
value in all its competing uses and should be recognized as an economic
good’ (Hanemann, 2006). Some authors define the economic value
of water as the amount that a rational user is willing to pay for a
publicly or privately supplied water resource (Ward and Michelsen,
2002).
The economic value of water used specifically for irrigation
results from the fact that it produces revenue to farmers through
their crop production and sales (Nikouei and Ward, 2013). The
World Bank has been advocating neoliberalist policies to reform the
The US High Plains are a sub-region of the Great Plains
and encompass Wyoming, southwestern South Dakota, western
Nebraska, eastern Colorado, western Kansas, eastern New Mexico,
western Oklahoma and northwestern Texas (Fig. 1).
Among the High Plains states, Texas, Kansas and Nebraska cover
the largest percentage of the area and provide the largest supply of
agricultural production in the region. For this reason and also due
to data paucity in the other HP states, Texas, Kansas and Nebraska
have been selected for the analysis presented in this paper.
In 2007, almost 50% of the area in the HP was used for crop cultivation. The main crops grown in the High Plains are corn, wheat,
hay, alfalfa, soybeans, cotton, and sorghum; with corn grown primarily in the Northern High Plains (NHP), wheat in the Central High
Plains (CHP), and cotton in the Southern High Plains (SHP) (U.S.
Department of Agriculture, 2008).
22
J.R. Ziolkowska / Agricultural Water Management 153 (2015) 20–31
9,000
8,000
Thousand acres
7,000
6,000
5,000
4,000
3,000
2,000
1,000
0
TX
KS
Sprinkler
NE
Microirrigation
Surface
Fig. 2. Irrigated acres in the High Plains states in 2005. Source: Author’s presentation
based on USGS (2013a,b). Legend: 1 acre = 0.4 ha
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
TX SHP
TX NHP
Corn
Cotton
KS HP
Wheat
Soybean
NE HP
Sorghum
Fig. 3. Percentage of water used in different regions of the High Plains for the
analyzed crops in 2010. Source: Author’s calculation based on USGS (2013b). Legend: TX NHP—Texas Northern High Plains, TX SHP—Texas Southern High Plains, KS
HP—Kansas High Plains, NE HP—Nebraska High Plains
Fig. 1. Case study region—High Plains.
The amount of necessary irrigation in the High Plains depends
first and foremost on the weather conditions and precipitation in
the respective growing seasons. In all analyzed HP states, precipitation varied considerably in the most recent years, with an extreme
drought in Texas in 2011 and in Nebraska in 2012. The average annual precipitation in the NHP (represented in this study by
Nebraska) amounted to 1236.9 mm in 2010 and 2011 and decreased
to 609.6 mm in 2012. The year 2010 was “average” in terms of
the total precipitation in Kansas and Texas. In 2011, the CHP
(Kansas) experienced a precipitation decrease down to 642.6 mm
and 726.4 mm in 2012. In the Southern High Plains (Texas), the precipitation amounted to 1145.5 mm in 2012, 325.1 mm in 2011 and
589.3 mm in 2012.
The extreme and unexpected droughts caused an increase in
demand for irrigation water and a decrease in agricultural yields at
the same time. Therefore, it is viable to compare different regions
with different water conditions in the same year and/or different
years.
In all analyzed states, irrigation occurs through sprinklers, surface (furrow irrigation) and micro-irrigation (drip irrigation). The
total irrigated acreage in 2005 was 6205.8 thousand acres (2511.3
thousand ha) in the Texas HP, 3119.3 thousand acres (1262.3 thousand ha) in the Kansas HP and 8350.61 thousand acres (3379.4
thousand ha) in the Nebraska HP, with sprinkler irrigation being
the prevailing method (Fig. 2). The highest percentage of irrigation
water used in the High Plains is groundwater with 78.5% of the
total irrigation water in Texas, 96% in Kansas and 86.5% in Nebraska
(USGS, 2013b). The amount of water used for irrigating the analyzed
crops in the respective states of the High Plains is variable. In 2010,
the highest percentage of water used in the Texas Northern HP for
the cultivation of the analyzed crops was applied to corn production (66%) and wheat production (21%). In the Texas Southern HP, of
the total water used for irrigating the analyzed crops, 54% of water
resources were applied to cotton production, 29% to corn production and 13% to wheat production. In the Kansas HP, the prevailing
crops in terms of the water use were corn (80%) and soybeans (15%).
Finally, in the Nebraska HP the most irrigation intensive crops were
corn (71% of water use among the analyzed crops) and soybeans
(26%) (Fig. 3).
For the purpose of the analysis and in order to provide a congruent comparison basis, five main crops grown in the analyzed
states have been selected: corn, cotton, soybeans, sorghum, and
wheat. The details about the selection and revenue budgets for the
respective crops in the analyzed High Plains states are elaborated
in Section 4.2.
4. Methodology and data
4.1. Rudiments of farm-budget residual valuation
Residual valuation method (RVM) is based on the production
function that represents the relation between inputs and outputs in
the crop production process. Production functions themselves are
difficult to estimate because they depend on several factors, such as
crop, weather conditions, geographical location, soil type, irrigation
system, management system, etc. Valuation of water in the production process is based on the idea that a profit-maximizing firm will
J.R. Ziolkowska / Agricultural Water Management 153 (2015) 20–31
use water up to the level where the net revenue gained from one
additional unit of water is equal to the marginal cost of obtaining
this water (Lange, 2006). The residual valuation satisfies two main
postulates that comply with neoclassical economic theory:
(1) Producers (here: farmers) optimize their production functions
and are able to forecast prices of output and inputs other than
water, and
(2) The total value of the product is assigned to each input factor
according to their marginal productivity to the point that the
total value of the product is exhausted (the value of marginal
products1 of the input factors is equal to the opportunity costs
of the input factors) (competitive equilibrium assumption)
(Young, 2005a,b).
According to the first postulate, the RVM defines the production
function with four production factors (capital—K, human labor—H,
land—L, and water—W). It assumes competitive factors and product
markets in which the value of the marginal product (VMP) of the
production factor equals its price (for each input factor i, Pi = VMPi )
(Young, 2005a; Young and Haveman, 1985) (Eq. (1)).
Y = f (XK , XH , XL , XW )
(1)
where Y—output of agricultural production; XK —capital input factor; XH —labor input factor; XL —land input factor; XW —water input
factor (irrigation water).
The total output is a function of capital factors (materials,
energy, equipment, and machines), human capital and labor, land
for farming, and water used for crop irrigation. Thus, residual valuation represents the value for the producer from producing a good,
expressed as the sum of the value of the inputs required to produce
it.
Residual valuation assumes that all markets are competitive,
except for the water market. Therefore, the total value of production is equal to the opportunity costs of all the inputs. From this
perspective, the residual value of water can be estimated even
when water is a scarce resource and crops are irrigated with a deficit
or supplementary irrigation, because water value is assigned the
residual value once the remaining inputs get assigned the opportunity or market cost (Berbel et al., 2011).
In this analysis, all input variables are known except for the
price of water (PWi ), which can be estimated based on the other
known variables of the remaining input factors in the production
function. Because the model assumes competitive factor and product markets, prices can be treated as known constants. Thus, the
total economic value of the product (net returns to water) can be
expressed by subtracting the value of non-water input factors (capital, labor, land) (representing production costs) from the total value
of production generated upon irrigation (production revenues) (Eq.
(2)). For a single crop for one year, the net return represents the willingness to pay per acre for water delivered to the farm gate (Young,
2005a) or (in other words) the maximum amount the producer
could pay for water and still breakeven.
RW (PWi × QWi ) = Yi × Pi − (QKi × PKi + QHi × PHi + QLi × PLi )
(2)
where PWi —shadow price of water (unknown and to be estimated), QWi —amount of water applied for the crop production;
RW —net returns to water; Yi —quantity of crop output (bu/ac)
[bushel/acre]; Pi —price received for crop i; Yi × Pi —total production (output), QKi —quantity of capital input; PKi —price of capital
input; QHi —quantity of human labor input; QLi —price of human
1
Value of marginal product (VMP) measures a firm’s revenue contributed by the
last unit of a production factor. VMP is calculated as the marginal output of the input
factor multiplied by the unit price of the output.
23
labor; PLi —area of land applied to produce the crop; PLi —price of
land used.
Assuming that all variables are known other than for water price
(PWi ), the unit price of water for irrigation (e.g., $/af) can be calculated as the quotient of the net returns to water per acre and the
amount of water used for irrigation (acre feet) (QW ) (Eq. (3)).
PWi∗ =
Yi × Pi − (QKi × PKi + QHi × PHi + QLi × PLi )
QWi
(3)
4.2. Data and state specific assumptions for the High Plains
residual valuation
Data used for the analysis (crop budgets, area of harvested crops
and water used for irrigation) were collected from the National
Agricultural Statistics Service (NASS), Texas Water Development
Board (TWDB), Texas AgriLife Extension Agricultural Economics
Station, Kansas Department of Agriculture, Kansas AgManager.Info,
and the University of Nebraska-Lincoln.
The Texas High Plains are represented by the Texas Northern High Plains and Southern High Plains, as differentiated by the
Texas Water Development Board and the regional Water Planning
Groups. In Kansas and Nebraska, only selected counties have been
included in the analysis due to their geographical location in the
High Plains. A graphical coverage of the selected counties included
in the analysis is displayed in Figs. 4–6.
For the 2011 analysis for Texas, two assumptions were made:
(a) For the Northern High Plains, the acreage of harvested cotton
and sorghum in 2011 was assumed to be equal to the 2012
estimates due to the missing data for 2011;
(b) For the Southern High Plains, the harvest acreage of irrigated
soybean in 2011 was assumed to amount to the acreage in 2010
due to missing data for the following years.
The total area harvested in 2010 in the respective states has been
derived from the NASS statistics. For the residual valuation, production revenues and costs are the most fundamental elements of the
analysis. The estimated returns per acre of crop production were
based on the actual crop yields and market prices in 2010 and 2011.
For Kansas, the returns also accounted for governmental subsidies
that differed slightly depending on the projected yield. For calculation purposes, subsidies were averaged for all crops, amounting
to $30/ac. The production returns for Texas and Nebraska do not
include crop production subsidies, as they are not granted in those
states. The paper depicts an actual market situation in 2010 and
2011. For this reason, the crop production subsidies were consistently included in the analysis in the given years as they were
applied in the analyzed regions. Similarly, they were not included
in the net revenue calculations for the regions that did not receive
those subsidies in the analyzed years. An inclusion or exclusion of
subsidies in all regions would create an untrue market picture and
thus, it was avoided in the first place.
The production costs were based on the crop budgets for each
state/agricultural region. The production costs included in the analysis comprised direct and fixed expenses. Direct expenses consisted
of seed and fertilizer expenses, crop insurance, machine operator
labor and hand irrigation labor, fuel, gasoline as well as repair and
maintenance costs. The fixed expenses included costs of purchasing
implements, machines and other equipment (Appendix A).
While the crop budgets for Texas and Nebraska allow for estimating costs per acre, accounting for costs in Kansas was more
challenging. In this case, the crop production costs were based on
the estimated/projected budgets and were further adjusted according to the actual production yields (different than in the projected
crop budgets). The cost data for Nebraska represent one among the
24
J.R. Ziolkowska / Agricultural Water Management 153 (2015) 20–31
Fig. 4. Nebraska High Plains counties considered in the analysis.
possible planting and harvesting techniques applied on the fields.
Thus, estimates of total net production returns and the shadow
price of water need to be understood and interpreted accordingly.
Hence, a change in the harvesting technology or irrigation system
would result in a change of the total net returns and subsequently
the shadow price of water. Therefore, estimates of the shadow
price vary and depend on many factors (e.g., climatic conditions,
geographical location, irrigation system, planting, growing and harvesting systems, application of machines and tools, etc.).
Due to the paucity of data, cotton production in Nebraska
and Kansas was calculated only for cotton lint, while production
included cotton lint and cottonseed for Texas, as the cost estimate was conducted for both products. Excluding one of them
from the returns (but maintaining both of them in the cost analysis) would create a biased and unprofitable cotton production. For
Kansas, the area of irrigated soybean and sorghum harvested in
2010 was calculated based on the percentage estimates for irrigated
and non-irrigated crops in 2009. This was necessary due to missing
differentiation between irrigated and non-irrigated soybeans and
sorghum in 2010.
The land rents included in the production function were derived
from the crop budgets for the respective states, and are principally
accounted for in the cost calculation. The land charge/rent related
to land resources expresses the opportunity cost for owned land
or approximate cash rental rate for leased land, which is normally
around 2–6% of the land value. Different approaches are available
to calculate and set land charges; for instance, they can be based on
average market land rental charges, capitalized land values based
on landowner choice of the interest rate, average yields, or return
on investment. The land charge includes any land preparation costs,
also energy and water. Thus, the question arises about accounting
for water in residual valuation and the ways of avoiding doublecounting. This analysis acknowledges that water is used as one of
the land preparation practices; however, it also defines irrigation
based on the factual water amount necessary to grow the analyzed
crops. Even though water might be used to serve the agricultural
land, water contained in the soil is not enough to secure proper
crop growth and development.
Data on water use for irrigation in Texas in 2010–2011 and
in Kansas was provided by the Texas Water Development Board
(TWDB, 2013) and the Kansas Department of Agriculture, respectively. No records of water use for irrigation of the respective crops
are available in Nebraska. Therefore, the amount of irrigation water
was calculated based on the actual water demand (Wd ) (and crop
requirements). For these calculations, the reference ETr (evapotranspiration) and crop coefficients (Kc ) were used, as well as
data on the average precipitation (Pr ) in the state. The total water
demand for growing the respective crops (expressing the total
water need of the given crops), neglecting changes in soil water
storage, has been calculated according to the formula:
Wd = ETr × Kc − Pr
(4)
The crop water demand based on the reference ETr has been
evaluated for Nebraska by several authors (Irmak et al., 2013, 2012;
Billesbach and Arkebauer, 2012). For this analysis, the seasonal
average ETr value of 4.4 mm/day (1606 mm/year) was applied for
the years 2009–2010 (Irmak et al., 2013). The crop coefficients
were calculated as an average for each growing stage for each crop
(Irmak, 2009). The precipitation for Nebraska in 2010 (584 mm)
was averaged based on monthly estimates and data from the Prism
model (Prism Climate Group, 2013). The final water demand (in af)
for each crop and all growing stages was calculated by multiplying
J.R. Ziolkowska / Agricultural Water Management 153 (2015) 20–31
25
Fig. 5. Kansas High Plains counties considered in the analysis.
the annual water demand with the total area harvested. It was further used as the estimate of the water applied for irrigation of the
respective crops in the state.
4.3. Shadow price of water
To estimate the shadow price of water for irrigation, revenue
estimates for 2010 were calculated as a product of the crop price
per production unit ($/bushel) and the production quantity/yield
(bushels) for each crop separately. The cost estimates per acre of
crop production in 2010 were derived from the state crop budgets,
for the respective crops and states. The total revenues and costs
were calculated as the products of the total area of the harvested
crops in the state and the unit revenues and unit costs (per acre),
respectively. The net returns to water were further calculated as
a difference between the total revenues and costs. Because crop
budgets are calculated at the state level, they generally differ in
terms of input variables, also depending on the crop production
area and climatic conditions. However, they need to be included
consistently as generated for the state, because exclusions of certain input variables could potentially distort the data sets and cause
methodological biases in the end.
The ratio between the net returns to water and the total amount
of water used for irrigating the respective crops (in acre feet) allows
for estimating the shadow price of water for irrigation (in $/af). The
shadow price of water has been estimated for the year 2010 for
Texas, Kansas and Nebraska, while a two-year case-study analysis
(2010 – wet year vs. 2011 – dry year) has been conducted in addition for Texas. Missing data on water use for irrigation in Kansas
and Nebraska did not allow conducting the two-year case study for
those states.
5. Results and discussion
5.1. Shadow price of water in the High Plains of Texas, Kansas
and Nebraska
The final calculation variables and the shadow price values of
irrigation water in $ per acre foot ($/af) for the analyzed crops in
the High Plains are displayed in Table 1. To facilitate comparison
of research results with other studies, the final shadow price of
water was also expressed in $/m3 . The shadow price of water for
irrigation in Texas in 2010 is higher in the Texas Northern High
Plains than in the Texas Southern High Plains for all analyzed crops.
The amounts of water used for irrigating different crops in both
regions are similar, except for cotton, which dominates water use
in the Texas Southern High Plains. This indicates that agriculture
earns higher production net returns for each crop in the northern
part of the Texas Panhandle. For some crops both in the Northern
and the Southern Texas HP, the net returns have negative values
and thus impact the final value of the shadow price. A negative
value of the shadow price means that an increase in the water use
for irrigating wheat and sorghum by one acre foot, would result in
the decrease of net returns by the respective shadow price values.
In the Texas Northern High Plains, the shadow price of water for
corn amounted to $92.02/af ($0.07/m3 ), which denotes the maximum a farmer could pay for water and still cover costs of the crop
production. The highest shadow price of $856.99/af ($0.70/m3 ) was
found for cotton, while the lowest price of $18.61/af ($0.02/m3 ) for
soybeans. The shadow price for wheat and sorghum are negative,
which results from the negative net production revenues indicating
production unprofitability for those crops in the given year. In this
paper, only positive shadow values are interpreted. The negative
shadow values are not considered as the lowest values, because
26
J.R. Ziolkowska / Agricultural Water Management 153 (2015) 20–31
Fig. 6. Texas High Plains counties considered in the analysis.
they are not desirable for farmers and the agricultural production
as a whole.
When comparing the Texas High Plains with the Kansas and
Nebraska High Plains in 2010, it becomes apparent that the shadow
Table 1
Shadow price of water for irrigation in the High Plains in 2010.
Crop
Net returns
(million $)
Texas HP
Northern HP
Corn
Cotton
Wheat
Soybeans
Sorghum
89.4
95.8
−52.8
0.38
−10.3
Southern HP
Corn
Cotton
Wheat
Soybeans
Sorghum
Irrigation
water (in af)
Shadow price of
water ($/af) [$/m3 ]
971,853
110,663
309,361
20,486
61,906
92.02 [0.07]
865.99 [0.70]
−170.71 [−0.14]
18.61 [0.02]
−166.85 [−0.14]
3.4
81.5
−27.6
−17.5
−6.1
656,335
1,233,028
304,248
25,566
72,277
5.13 [0.004]
66.10 [0.05]
−90.73 [−0.07]
−685.17 [−0.56]
−84.94 [−0.07]
Kansas HP
Corn
Cotton
Wheat
Soybeans
Sorghum
103.9
1.4
−24.0
−5.0
−12.5
1,339,319
24,674
52,416
244,034
16,372
77.64 [0.06]
56.43 [0.05]
−458.35 [−0.37]
−20.74 [−0.02]
−765.10 [−0.54]
Nebraska HP
Corn
Wheat
Soybeans
Sorghum (total)
249.9
−2.2
434.7
−8.3
10,023,162
239,988
3,718,404
100,308
24.94 [0.02]
−9.04 [−0.01]
116.91 [0.09]
−82.85 [−0.07]
Source: Author’s calculation.
price values for irrigation water vary among different regions and
are directly determined by the net returns and the amount of
water applied for irrigation. For example, the highest net returns in
2010 were derived from soybeans production in Nebraska ($434.7
million), corn production in Kansas ($103.9 million) and cotton
production in the Texas Northern HP ($95.8 million) and Southern HP ($81.5 million). The lowest positive net returns were found
for soybean production in the Texas Northern HP ($0.38 million),
cotton production in Kansas ($1.4 million) and corn production in
the Texas Southern HP ($3.4 million). The negative net returns were
found for wheat and sorghum production in Nebraska as well as in
the Texas Northern HP, and for wheat, soybeans and sorghum production in the Texas Southern HP and Kansas. This denotes that the
production of those crops is unprofitable at the given crop prices
and the given irrigation level. Changing the level of irrigation for
those crops in 2010 would not necessarily impact net revenues considerably, but would directly influence the shadow price of water.
The presented results are comparable with other studies analyzing the price of water for irrigation at the regional level in
different countries and in different states in the US. Kelso et al.
(1973) estimated the marginal prices of water in 150 representative farms in Arizona, ranging between $4/af ($0.003/m3 ) for
grain sorghum and $236/af ($0.19/m3 ) for cotton. A study by
Martin and Snider (1979) for the Salt River area in Arizona indicated short-run marginal values ranging from $33/af ($0.03/m3 ) for
grain sorghum to $157/af ($0.13/m3 ) for lettuce to over $1280/af
($1.04/m3 ) for dry onions. Other studies using crop-water production functions for Arizona, New Mexico, California and other
states found the marginal prices of water to range between less
than $21/af ($0.02 m3 ) for grain sorghum in Arizona and $536/af
($0.43/m3 ) for tomatoes in California.
J.R. Ziolkowska / Agricultural Water Management 153 (2015) 20–31
5.2. Shadow price of water in the Texas High Plains in 2010 and
2011
Net returns for the analyzed crops in the Texas HP were higher
in 2010 than in 2011, which implies higher shadow prices in 2010.
This can be explained by several input variables used for calculating the shadow price of water. Due to the severe drought in
2011, the harvested areas for the respective crops were lower
in 2011 than in 2010, which had a direct impact on the production revenue per acre. Similarly, yields in bushels per acre
decreased in 2011 compared to 2010 (Table 3), which resulted in
an increase of the crop prices per bushel (Table 2). At the same
time, total water use for crop irrigation increased in 2011 relative to 2010 (Fig. 7), which consequently resulted in lower net
production returns. In a dry year, due to increased irrigation, the
marginal value of water was lower than in a wet year, and consequently the gap between the water rates and the water value
decreased which resulted in a lower shadow price. The changes
occurring in a drought year and the resulting impacts are visualized below.
600,000
500,000
In acre feet
As emphasized in the previous chapters, shadow prices of water
for irrigation vary because of several factors included in the production function and the respective cost and revenue budgets in
different states and regions. Therefore, strict comparability of the
estimated shadow price values is limited, especially when studies
are based on the crop budget projections only and do not include
the actual values of the input variables. While the crop budgets
provide a rough estimate of the expected/anticipated yields, prices
and costs in the coming year, they are not designed and updated
retrospectively based on observed weather events and market
developments. According to Bush and Martin (1986), crop prices
received by farmers are the predominant factor in determining the
marginal value of water for irrigation. In addition, changes in energy
costs of pumping and pumping lifts also play an important role in
determining whether farmers would breakeven while paying for
irrigation water. This paper is a reality check for the crop budget
projections as it depicts the actual market situation by including
the observed crop prices, yields and the harvested area (on the net
return side), while the costs are based on the crop budgets adjusted
to the actual yields in the analyzed years and states. The analysis
shows that, in many cases, the agricultural production in 2010 and
2011 was unprofitable, and thus the shadow prices of water for
irrigation were negative.
The variability of the shadow prices largely depends on the crop
market prices that are varying mainly due to changing weather
conditions, such as drought, represented in this paper with the
year 2011 for Texas. Table 2 presents the crop prices for the analyzed crops in Texas, Kansas and Nebraska. Even though this section
displays shadow prices of irrigation water solely in 2010, the
dimensions of expected changes subject to changes in crop prices
can be anticipated. A detailed study of those changes is presented
in Section 5.2 for the Texas High Plains.
0
Corn
Water availability
Wheat
Soybean
Sorghum
Crops
Northern HP
Southern HP
Fig. 7. Difference in water use for irrigation between 2010 and 2011 in the Texas
High Plains. Source: Author’s calculation based on TWDB (2013). Legend: 1 acre foot
(af) = 1233.5 m3
In 2011, approximately 500,000 af of additional water was necessary to provide production of cotton in the Texas Southern High
Plains, while only 265,000 af additional water in the Texas Northern High Plains. Also 425,000 af of additional water was applied in
2011 (compared to 2010) for corn production in the Texas Northern
High Plains, while the water use in the Texas Southern High Plains
for corn production was only slightly higher (by roughly 25,000 af)
than in 2010. The demand for additional irrigation water for wheat
and sorghum production in 2011 was similar in the Texas Northern
and Southern High Plains, respectively. However, the demand for
water for soybeans production decreased in 2011 by ∼13,200 af in
the Texas Southern High Plains compared to 2010, mainly due to a
considerable reduction of the planted and harvested area.
The analysis of the shadow prices of water for irrigation in Texas
shows that the shadow prices were, in most cases, lower in 2011
compared to 2010 for the majority of the analyzed crops in both
the Northern and Southern High Plains in Texas (Table 3).
The differences between the years 2010 and 2011 have been
directly determined by the net returns (the underlying crop prices
and yields) as well as the amount of water applied for irrigation.
The shadow price for soybeans in the Texas Northern High Plains
decreased from a positive value in 2010 ($18.61/af) ($0.02/m3 )
down to a negative value in 2011. In the Texas Northern HP, both
the net returns and further the shadow price of water for irrigation
were negative for wheat, soybeans and sorghum production in
2011, while only for wheat and sorghum in 2010. The highest
decrease of the production net returns between 2011 and 2010,
and the shadow price of water, was found for the cotton production in the Texas Northern HP ($73.20/af down from $865.99/af)
($0.06/m3 down from $0.70/m3 ). Another significant change in the
shadow price of water occurred for soybean production in the Texas
Northern HP [decrease from $18.61/af ($0.02/m3 ) in 2010 down
to $−26.37/af ($−0.02/m3 ) in 2011] and cotton production in the
Narrowing gap between water
rates and water value/unit
Water rates
Cotton
-100,000
Marginal value of
water/unit
According to the economic resource theory:
300,000
100,000
Net returns
Shadow price of water
400,000
200,000
In a drought year, the following changes have been observed:
Water use for irrigation
27
28
J.R. Ziolkowska / Agricultural Water Management 153 (2015) 20–31
Table 2
Crop prices in Texas, Kansas and Nebraska in 2010 and 2011 (in $/bu).
Texas
Kansas
Nebraska
Crop*
2010
2011
2010
2011
2010
2011
Corn
Cotton upland**
Cotton seed
Wheat
Soybeans
Sorghum***
4.10
0.70
155.33
5.25
8.60
6.81
6.38
0.86
239.14
7.49
11.35
10.84
3.83
0.70
–
5.11
10.00
6.35
6.33
0.80
–
7.42
12.67
11.13
3.83
–
–
4.82
9.82
6.32
5.92
–
–
6.87
12.33
10.94
Source: Author’s presentation based on National Agricultural Statistics Service (NASS) (2013), AgManager Info (2010) and AgriLife Extension (2013).
*
All prices are expressed in $/bu, except for the specifications as below.
**
Price per lint ($/lb), lb—pound (1 pound = 0.45 kg).
***
Sorghum $/cwt (100 pounds).
Table 3
Shadow price of water for irrigation in the Texas High Plains in 2010 and 2011.
Crop
Yield (bu/ac)
Net returns (mi $)
Shadow price of water ($/af) [$/m3 ]
Yield (bu/ac)
Shadow price of water ($/af) [$/m3 ]
89.4
95.8
−52.8
0.38
−10.3
92.02 [0.07]
865.99 [0.70]
−170.71 [−0.14]
18.61 [0.02]
−166.85 [−0.14]
2011
138.4
667.8
173.1
34.1
75.0
53.7
27.5
−32.8
−0.5
−7.6
38.46 [0.03]
73.20 [0.06]
−70.14 [−0.06]
−26.37 [−0.02]
−85.53 [−0.07]
3.4
81.5
−27.6
−17.5
−6.1
5.13 [0.004]
66.10 [0.05]
−90.73 [−0.07]
−685.17 [−0.56]
−84.94 [−0.07]
142.1
499.7
164.4
40.0
60.0
3.4
−228.5
−16.8
−6.4
−11.4
5.01 [0.004]
−129.31 [−0.10]
−40.87 [−0.03]
−520.43 [−0.42]
−135.54 [−0.11]
Net returns (mi $)
Northern HP
2010
Corn
214.5
Cotton
1111.8
Wheat
187.6
Soybeans
55.1
Sorghum
102.6
Southern HP
194.8
Corn
953.7
Cotton
176.4
Wheat
35.0
Soybeans
82.1
Sorghum
Source: Author’s calculation.
Southern High Plains wheat, soybeans and sorghum indicated
negative values of the shadow price in both scenarios in 2010 that
were determined mainly by the negative net returns to water and
an increased amount of water applied for irrigating those crops
(Figs. 8 and 9). The scenario with actual net revenues clearly deviates from the long-term predictions and anticipations and displays
actual conditions of the recent drought. Evaluating the shadow
price of water for irrigation under the condition of extreme weather
events can be useful for predicting the economic value of water in
the future, in case the drought persists or recurs. This again would
allow farmers to prepare for lower production revenues in general,
while the shadow price of water could be used for designing water
management policies and emergency programs in the situation of
drought.
The analysis shows a high variability of the estimated shadow
price values for crop production and the related uncertainty subject
to changes of crop market prices. Moreover, it shows that farmers
1000
800
600
$/af
Texas Southern HP [decrease from $66.10/af ($0.05/m3 ) in 2010
to $−129.31/af ($−0.10/m3 ) in 2011]. The smallest changes in the
Texas Southern HP occurred for the corn production [decrease from
$5.13/af ($0.004/m3 ) to $5.01/af].
The decreasing trend of the shadow price of water for irrigation
for the most analyzed crops directly corresponds with the extreme
drought in Texas in 2011. The lower shadow price of water in a
drought year indicates that farmers would not be able to pay as
much for water in 2011 than in 2010 and still breakeven, because
of their diminished revenue incomes from crop production. The
higher crop prices in 2011 were not sufficiently high to leverage
the detrimental effects of drought.
Moreover, in order to compare the shadow price based on the
actual net revenues with the shadow price for irrigation based on
expected net revenues, new scenarios have been included in the
analysis. The calculation of the shadow price based on the actual
net revenues – as presented in this paper – includes the production
function parameters (crop prices, production yields, production
costs) as they occurred in a given year. The shadow price based
on expected net revenues shows a theoretical scenario including
production function parameters estimated in advance by regional
agricultural extension stations and that did not foresee unexpected
weather events like drought. It expresses the shadow price of water
based on production trends from the past under normal production
conditions.
The analysis for 2010 and 2011 shows that the shadow price
of water in the scenario with expected net revenues is higher for
most crops in the Texas Northern and Southern High Plains than
the shadow price of water in the scenario with actual net revenues.
This clearly describes the situation of the drought year with lower
than usual net returns to water and higher water use for irrigation.
An exception constitutes cotton in 2010 where the shadow price
of water in the scenario with the actual net revenues was higher
than in the scenario with the expected net revenues. In the Texas
400
200
0
-200
-400
Corn
Expected 2010
Cotton
Wheat
Actual 2010
Soybeans
Expected 2011
Sorghum
Actual 2011
Fig. 8. Shadow price of water based on expected and actual net revenues in the
Texas Northern High Plains. Source: Author’s calculation
J.R. Ziolkowska / Agricultural Water Management 153 (2015) 20–31
Corn
Cotton
Wheat
Soybeans
Sorghum
200
$/af
0
-200
-400
-600
-800
Expected 2010
Actual 2010
Expected 2011
Actual 2011
Fig. 9. Shadow price of water based on expected and actual net revenues in the
Texas Southern High Plains. Source: Author’s calculation.
could theoretically be paying much higher prices for irrigation
water than they are currently paying and would still be able to reach
the break-even point. Wheat and sorghum production in the Texas
High Plains and wheat, soybeans and sorghum production in the
Texas Southern High Plains were not profitable in 2010. The following 2011 drought year resulted in an even higher profitability loss
for soybeans production in the Texas Northern HP and for cotton
production in the Texas Southern HP. Those results are in sync with
the general trend of agricultural losses due to the Texas drought as
evaluated, for instance, by Fannin (2012) and Guerrero (2011). In
this context, the question of crop subsidies arises (as applied in
some states, e.g., Kansas) and the need of a more balanced policy
that would ensure profitable crop production on the one hand and
efficient water allocation and water pricing on the other.
The results presented in this section represent only two years:
2010 – a normal wet year and 2011 – a dry year. In order to provide
a direct decision support to farmers and help them decide about
their cropping plans, an analysis comprising several years would be
recommended to detect profitability of the given crop production
in the long-term. Due to data paucity, a congruent analysis of this
kind is not possible at this point of time for the analyzed region.
6. Limitations and challenges of residual valuation
Despite its broad applicability, the residual valuation approach
has several limitations. Any inputs not accounted for in the analysis
are considered as the ‘unknown input’ of water. Thus, the value of
water derived from the residual valuation must be considered to be
a maximum value. Moreover, any errors in the prices and quantities of the inputs or outputs will incorrectly account for the value of
water. In addition, the method does not allow for an extended spatial analysis and can pose an aggregation problem when different
regions characterized by different yields and/or different production factor inputs are considered. Currently, there is no methodological solution to these limitations (Hellegers and Davidson,
2010). As the advantages of applying residual valuation override
the disadvantages, after limiting possible biases in the input data,
the method has been acknowledged as a viable valuation approach.
In addition, the net returns to water are variable and depend on
market dynamics mainly due to crop price and yield changes. This
should not be seen as a disadvantage of the methodology, but rather
as a way of depicting the real changes occurring on the market in a
given year and implications of possible market distortions subject
to economic shocks like, for instance, the 2011 drought.
Despite the limitations, several challenges also exist. One of
them is data paucity and/or data inconsistency, especially when
comparing results from different geographical regions, as is the
case with this study for the High Plains. One of the major inconsistencies relates to crop budgets that can include different cost
components in different states and regions, one of them being crop
29
subsidies. This challenge is a relevant policy topic at the same time.
According to the results, even subsidized crops showed negative
production returns in 2010, while the lack of subsidies could cause
the production of those crops to be unprofitable in the long-term.
Another challenging question is the land charges/rents included in
the cost calculations in the crop budgets. In most cases, the crop
budgets do not indicate the method of calculation, which can be
crucial for determining both the energy and water used to maintain certain soil moisture of a given land property. Depending on
the definition of the land rents, this could cause either under- or
over-estimation of water used on the crop land. Thus, a clear definition of land rents and the related assumptions should be included
when estimating the economic value of water for irrigation.
In addition, water demand functions for different regions are
difficult to estimate directly based on quantities of water used for
irrigation at different price levels (Colby, 1989). Also, externalities
(e.g., spillover effects between crop markets and other sectors),
trade balances as discussed by other studies, e.g., Liu et al. (2009)
and dynamic evaluations (He et al., 2006) are not covered with the
residual valuation.
7. Conclusions and perspectives
The analysis presented in this paper points out the crops
that were unprofitable in 2010 and shows the estimated prices
that could be potentially placed on water. Even though higher
water rates for irrigation would be beneficial for the environment
and conserving water resources, it would cause severe economic
impacts on farms’ productivity and would further reduce agricultural production. Wheat and sorghum production in the Texas
Northern HP and in the Nebraska HP, as well as wheat, soybeans
and sorghum production in the Texas Southern HP and the Kansas
HP were unprofitable in 2010 and thus the estimated shadow prices
of water for those crops were negative.
Also, net returns and the final shadow prices of water were
impacted by significant changes in the crop market prices between
2010 and 2011, due to the 2011 drought. Drought caused an
increase in the amount of water used for irrigation, both in the
Texas Northern and Southern High Plains as well as net return
losses for several crops. The analysis also shows variations in the net
returns and subsequently the shadow price of water for irrigation,
regardless of the wet and dry production year (2010 and 2011,
respectively). This can be explained by the fact that the net production returns depend on several input factors, while the agricultural
production (and yields) are uncertain and highly variable in different years. It needs to be mentioned that the year 2011 was unusual
in terms of climatic conditions that directly impacted the agricultural net production returns. An analysis of production returns
over a long-term time span is anticipated to reveal positive net production revenues and thus positive shadow price values of water.
Because the actual value of water is difficult to measure and
the estimates are variable due to changing production conditions,
a valid concern about overestimating the value of water could be
raised. However, in the case of agricultural production, this aspect
should not be alarming. If, theoretically, water rates are higher than
the real value of water for crop production, two scenarios could
occur:
(a) Irrigation would be abandoned and farmers would switch to
non-irrigated crops, or
(b) Investments in new technologies and crops with higher added
value (trees, horticulture) would be necessary to set the value
of water higher than the cost of provision (Berbel et al., 2011).
As currently given, the water rates for irrigation are very low and
the scenarios mentioned above are possible to happen, however,
30
J.R. Ziolkowska / Agricultural Water Management 153 (2015) 20–31
mainly due to water scarcity and persistent drought rather than
to the current water rates for irrigation alone. However, drought
conditions are a triggering factor for concerted actions and alternative water management strategies and policies. Estimating the
shadow price of water allows for depicting the scope of the existing problem of underpricing water resources, however, it does not
solve the problem directly. Interactive decision making and round
table panel discussions are necessary for stakeholders and policy
makers to address the urgent question of pricing water at a level
that would both secure farmers’ incomes and at the same time help
conserve water resources.
Nowadays, a policy-driven rise in water rates for irrigation
could result in negative economic implications for farmers and
agricultural productivity in the long-term. The presented evaluation approach could help stakeholders and policy makers to assess
scenarios and tradeoffs between efficient production levels and
environmental protection levels of water resources. In this way,
strategies could be designed on how to implement higher water
rates without considerably hindering current market transactions.
The results generated with the analysis can be used to
further evaluate other intertwined problems, e.g., the lost production opportunities if water is allocated for other purposes than
irrigation, property rights associated with changing the use of
water, optimization of water use in the agricultural sector and
integrated water resource management (IWRM). As IWRM sets
forth treating water as an economic good and setting a price that
reflects its value, the presented study could be used as an economic
instrument to help local governments and stakeholders promote
conservation of water resources.
Acknowledgments
The author thanks the Editor and the anonymous reviewers for
their valuable comments that helped improve the paper. The author
also thanks the representatives from the Texas Water Development
Board, Texas A&M AgriLife Extension Service, High Plains Regional
Climate Center and Kansas State University for providing necessary
information and data. The author appreciates Bob Reedy and Di
Long for their help and support.
The research was supported by the State of Texas Advanced
Resource Recovery Program and the Oklahoma NSF EPSCoR Program. The initial work has been conducted at the University of
Texas at Austin. The paper has been finalized and completed at the
University of Oklahoma.
Appendix A. Crop revenues and costs in Texas, Kansas and
Nebraska
Crop
Corn
Revenues/ac
Costs/ac*
Cotton
Revenues/ac
Costs/ac
Wheat
Revenues/ac
Costs/ac
Soybeans
Revenues/ac
Costs/ac
Sorghum
Revenues/ac
Costs/ac
Texas NHP
Texas SHP
Kansas
Nebraska
2010
2011
2010
2011
2010
2010
879.5
772.9
883.0
800.1
798.6
749.0
906.6
836.1
748.2
681.9
710.8
663.1
906.6
719.5
772.1
720.9
789.6
719.8
555.1
787.9
580.9
547.9
–
329.4
433.8
336.4
433.7
259.7
438.9
267.1
504.7
323.8
375.9
284.4
304.0
473.9
448.1
387.0
444.6
301.0
452.0
454.0
509.4
355.0
366.9
582.3
389.3
391.9
478.7
456.8
495.7
313.6
459.7
365.4
526.6
315.0
406.5
319.5
432.7
Source: Author’s calculation.
*
Costs include both fixed and variable costs.
References
AgManager Info, Crops Projected Budgets: Center-pivot Irrigated Crops,
http://www.agmanager.info/crops/budgets/proj budget/irrigated/
2010,
(09/02/2013).
AgriLife Extension, 2013. Crop Budgets. AgriLife Extension, Texas, http://agecoext.
tamu.edu/resources/crop-livestock-budgets/ (07/11/2013).
Berbel, J., Mesa-Jurado, M.A., Piston, J.M., 2011. Value of irrigation water in
Guadalquivir Basin (Spain) by residual value method. Water Resour. Manage.
25 (6), 1565–1579.
Billesbach, D.P., Arkebauer, T.J., 2012. First long-term, direct measurements of
evapotranspiration and surface water balance in the Nebraska SandHills. Agric.
For. Meteorol. 156, 104–110.
Bush, D., Martin, W., 1986. Potential costs and benefits to Arizona agriculture of the
Central Arizona Project. In: University of Arizona College of Agriculture Technical
Bulletin No. 254. University of Arizona College of Agriculture, Arizona.
Colby, B.G., 1989. Estimating the value of water in alternative uses. Nat. Resour. J.
29 (Spring), 511–527.
Euzen, A., Morehouse, B., 2011. Special issue introduction water: what values? Policy
Soc. 30, 237–247.
Fannin, B., 2012. Texas agricultural drought losses total $7.62 billion. In: Texas A&M
AgriLife Today, Updated 2011. http://today.agrilife.org/2012/03/21/updated2011-texas-agricultural-drought-losses-total-7-62-billion/ (03/01/2013).
Gomez-Limon, J.A., Arriaza, M., Berbel, J., 2002. Conflicting implementation of agricultural and water policies in irrigated areas in the EU. J. Agric. Econ. 53 (2),
259–281.
Gomez-Limon, J.A., 2004. Water pricing: analysis of differential impacts on heterogeneous farmers. Water Resour. Res. 40, W07-S05, 1–12.
Gomez-Limon, J.A., Riesgo, L., 2004. Irrigation water pricing: differential impacts on
irrigated farms. Agric. Econ. 31, 47–66.
Guerrero, B., 2011. The impact of agricultural drought losses on the Texas economy.
In: Briefing Paper. AgriLife Extension, Texas A&M System.
Hanemann, W.M., 2006. The economic conception of water. In: Rogers, P.P., Llamas,
M.R., Martinez-Cortina, L. (Eds.), Water Crisis: Myth or Reality? Taylor & Francis
plc, London.
He, J., Chen, X., Shi, Y., 2006. A dynamic approach to calculate shadow prices of water
resources for Nine Major Rivers in China. J. Syst. Sci. Complexity 19, 76–87.
Hellegers, P., Davidson, B., 2010. Determining the disaggregated economic value
of irrigation water in the Musi sub-basin in India. Agric. Water Manage. 97,
933–938.
Irmak, S., 2009. Estimating crop evapotranspiration from reference evapotranspiration and crop coefficients. In: NebGuide. University of Nebraska-Lincoln
Extension, Lincoln, NE.
Irmak, S., et al., 2013. Evapotranspiration crop coefficients for mixed riparian plant
community and transpiration crop coefficients for Common reed, Cottonwood
and Peach-leaf willow in the Platte River Basin, Nebraska—USA. J. Hydrol. 481,
177–190.
Irmak, S., Kabenge, I., Skaggs, K.E., Mutiibwa, D., 2012. Trend and magnitude of
changes in climate variables and reference evapotranspiration over 116-yr
period in the Platte River Basin, central Nebraska—USA. J. Hydrol. 420, 228–244.
Kelso, M., Martin, W., Mack, L., 1973. Water Supplies and Economic Growth in an
Arid Environment. University of Arizona Press, Arizona.
Lange, G.M., 2006. Case studies of water valuation in Namibia’s commercial farming
areas. In: Lange, G.M., Hassam, R. (Eds.), The Economics of Water Management
in Southern Africa: An Environmental Accounting Approach. Edward Elgar Publishing, Chelthenham, pp. 237–255.
Lilienfeld, A., Asmild, M., 2007. Estimation of excess water use in irrigated agriculture: a data envelopment analysis approach. Agric. Water Manage. 94 (1-3),
73–82.
Liu, X., Chen, X., Wang, S., 2009. Evaluating and predicting shadow prices of water
resources in China and its nine major river basins. Water Resour. Manage. 23
(8), 1467–1478.
Martin, M., Snider, G., 1979. Valuation of water and forage from the Salt-Verde Basin
of Arizona. In: Report to the U.S. Forest Service.
Mesa-Jurado, M.A., Martin-Ortega, J., Ruto, E., Berbel, J., 2012. The economic value of
guaranteed water supply for irrigation under scarcity conditions. Agric. Water
Manage. 113, 10–18.
National Agricultural Statistics Service (NASS), 2013. Data and Statistics. National
Agricultural Statistics Service (NASS), http://quickstats.nass.usda.gov/
(11/20/2013).
Nikouei, A., Ward, F.A., 2013. Pricing irrigation water for drought adaptation in Iran.
J. Hydrol. 503, 29–46.
Prism Climate Group, 2013. Climate variable: precipitation. In: Data Base. Prism
Climate Group, http://www.prism.oregonstate.edu/normals/ (10/02/2013).
Rigby, D., Alcon, F., Burton, M., 2010. Supply uncertainty and the economic value of
irrigation water. Eur. Rev. Agric. Econ. 37 (1), 97–117.
Schloss, J.A., Wilson, B.B., Buddemeier, R.W., 2000. Changes in Use Necessary for
Sustainability, Atlas of the Kansas High Plains Aquifer. http://www.kgs.ku.
edu/HighPlains/atlas/atsust.htm (10/16/2013).
TWDB, 2013. High Plains irrigation water use. In: Direct Data Request. TWDB (April
2013).
U.S. Department of Agriculture, 2008. Cropland Data Layer, 2008 ver.: USDA
National Agricultural Statistics Service Digital Data. U.S. Department of Agriculture, http://datagateway.nrcs.usda.gov/GDGOrder.aspx?order=QuickState
(01/10/2011).
J.R. Ziolkowska / Agricultural Water Management 153 (2015) 20–31
UN Water, 2013. Water Use. UN Water, http://www.unwater.org/statistics use.html
(10/16/2013).
US Environmental Protection Agency (EPA), 2012. The Importance of Water to the
U.S. Economy. Part 1: Background Report. Public Review Draft. EPA, September
http://water.epa.gov/action/importanceofwater/upload/Background2012.
Report-Public-Review-Draft-2.pdf (10/16/2013).
US EPA, 2013. The importance of water to the U.S. economy. In: Synthesis Report.
EPA, Washington, DC, November 2013.
US EPA, 2007. 1992 USGS National Land Cover Data (NLCD). EPA, http://www.epa.
gov/mrlc/nlcd.html (01/15/2014).
USGS, 2013a. Estimated Use of Water in the United States—County-Level Data for
2005. USGS, http://water.usgs.gov/watuse/data/2005/ (11/06/2013).
31
USGS, 2013b. High Plains Water-Level Monitoring Study (Groundwater Resources
USGS,
http://ne.water.usgs.gov/ogw/hpwlms/physsett.html
Program).
(01/15/2014).
Ward, F.A., Michelsen, A., 2002. The economic value of water in agriculture: concepts
and policy applications. Water Policy 4, 423–446.
Young, R., 2005a. Nonmarket economic valuation for irrigation water policy
decisions: some methodological issues. J. Contemp. Water Res. Educ. 131,
21–25.
Young, R., 2005b. Determining the Economic Value of Water: Concepts and Methods.
Taylor and Francis, UK.
Young, R., Haveman, R., 1985. Economics of water resources: a survey. Handbook of
Natural Resource and Energy Economics, vol. 2., pp. 465–529 (Chapter 11).