Securing food supply for future generations through groundwater management: a policy analysis approach.
Almas, Lal K. ; Taylor, Robert H. ; Guerrero, Bridget 等
Introduction: Translocation of businesses and population to the
Western United States are resulting in increasing municipal and
industrial water demand. However, the demand for irrigation still
dominates water use in the Western United States. Irrigation is the
largest single water user in the United States, often reaching 90
percent of total water consumption in the western states. Limited water
supplies and increasing demand are forcing the western states to
initiate long-range water planning and management to ensure that
adequate supplies are available in the future.
Texas is no different from other western states in facing the water
allocation dilemma. In particular, the Texas Panhandle personifies the
problem. The Texas Panhandle is not only faced with an expanding
urban/industrial sector, but the livestock industry has which been
expanding at an impressive rate, further compounding the water
allocation problem.
The area's temperate climate, sparse population and
environmental conditions such as low rainfall and deep water tables
favor concentrated livestock. More than 30 percent of the nation's
fed beef supply is produced in the Texas Panhandle and the surrounding
counties in New Mexico and Oklahoma (TCFA, 2009). The number of fed
cattle in Texas has increased from 3.1 million head in 1970 to 5.7
million head in 2007 (TASS, 2007). Out of 5.7 million head fed and
marketed in 2007 in Texas, 4.9 million head were from the Texas
Panhandle area (TASS, 2007). Fed cattle numbers in the Texas Panhandle
exploded in the late sixties and early seventies. Since then fed cattle
numbers have steadily grown.
The same conditions that have brought the cattle industry to the
area have recently attracting the hog industry to the region. The swine
industry in this area has expanded more than tenfold during the 1990s.
Growth of the swine industry has continued in the area with the opening
of Texas Farms in Perryton, Texas. It is anticipated that many satellite
industries will come to the area to support the expanding swine industry
increasing the demand on existing water supplies. The dairy industry in
the region is currently seeing a similar if not more impressive
expansion.
The growing livestock industry in the region has increased the
demand for feed grains in the area. In fact, the area has become a grain
deficit production region resulting in premiums to be paid for feed
grains. This expanding demand for feed grain has enticed area producers
to increase irrigation and place more acres under cultivation. The
result has been increased water demand for irrigation. Since there is no
renewable surface source of irrigation water in the Panhandle and only
limited recharge of the Ogallala aquifer in this area, irrigation water
is a fixed supply and excessive pumping results in shortening the
economic life of the aquifer and reduces the returns to the resources
held by the farmer (Amosson et al. 2001).
The increased need for water conservation in the region has also
led to the adoption of more efficient irrigation equipment such as the
use of low-energy-application (LEPA) and low-energy-spray-application
(LESA) systems (Howell, 2001). However, the efficiency improvements with
the new irrigation technology have resulted in lowering the marginal
cost to irrigate ($/unit of water applied). This reduced cost in turn
encourages producers to continue and sometimes expand irrigation often
resulting in more water use than before.
The main goal of any conservation policy is to limit the use of a
resource in an effort to preserve the quantity and quality of that
resource. Policies for conserving groundwater are aimed at preventing
depletions of the aquifer in an effort to assure a continued supply of
water for many years. This is very important when a region is rural in
nature and in which the local economy is dependent on agriculture. Such
is the case in the Texas Panhandle, with area municipalities also
relying on the aquifer to meet as much as 50% of the water needs for
other uses in addition to agriculture. In choosing an appropriate
policy, the benefits (in this case decreased drawdown of the aquifer)
need to be weighed against the costs (reduced producer and resource
supplier revenues because of reduced irrigated crop acres).
The scope of this study is the evaluation of a baseline and five
alternative policies designed to conserve groundwater in eight counties
in the Texas Panhandle. These counties were selected because they showed
a significant amount of water depletion in the baseline scenario during
a sixty-year simulation. This study focuses on the changes in saturated
thickness, water use (both in terms of acre-inches applied per acre and
total water use in acre-feet), crop mix (irrigated versus dryland), and
the net present value of profits in the Texas Panhandle overlying the
Ogallala Aquifer over a sixty-year planning horizon. The results of the
study allow a comparison between the baseline and each of the five
policies in terms of water use reduction as well as the economic impacts
of the policies to affected producers as well as the regional economy.
Data Collection and Research Methodology: Park (2005) and
Adusumilli (2007) developed economic optimization models of the Ogallala
aquifer for the Texas Panhandle and Oklahoma Panhandle, respectively.
This study follows the previous groundwater studies conducted in the
northern Texas High Plains but with a different approach. Dynamic
programming, an optimization technique, is used to develop optimization
models for each county in the study area. Dynamic programming procedure
is used to determine the optimal allocation of groundwater resource to
maximize the net returns from crop production. Wheeler (2006) developed
the framework for the dynamic optimization models under two different
irrigation systems, i.e., Low Energy Precision Application (LEPA) and
furrow for southern portion of the Ogallala aquifer in Texas and New
Mexico. This study follows the same framework but with only the LEPA
irrigation system taken into consideration. The model used in this study
is a dynamic model that considered crop production functions. Non-linear
dynamic programming with General Algebraic Modeling System (GAMS)
(Brooke et al. 2005) is used to facilitate multiple runs of the model.
In order to develop a non-linear programming model, the functional
relationship between yield and applied irrigation needs to be developed
for major crops in the region. The Crop Production and Management Model
(CroPMan) (Gerik and Harman 2003), a window based, multi-year,
multi-crop, daily time step cropping system simulation model, is used to
simulate the yields for the crops. Yields are simulated from CroPMan for
LEPA (90% efficiency) for major crops under varying water application
rates. Water response functions are estimated from the CroPMan data
using the quadratic functional form and express the relationship between
crop yield and total seasonal irrigation. With this function, decision
makers can assess irrigation water needs to meet production targets or,
conversely, estimate likely crop production for fixed volumes of water.
The optimization model incorporated the production functions from
CroPMan to develop a non-linear model. The developed models estimated
the optimal level of water required for irrigation and the resulting net
returns from crop production for major crops in the three counties over
a 60-year planning period. There is considerable yield variation from
year to year, especially for the lower irrigation frequencies. Although
the yield simulations are revealing, conversion of these yields to net
revenues gives a more complete picture of the merits of the various
irrigation levels. A three percent discount rate is used to calculate
the net present value for the 60-year period.
General Data Collection: Specific data are compiled for each county
within the study region. The county specific data included a five-year
average of planted acreage of cotton, corn, grain sorghum, and wheat,
total cropland and total acreage under irrigated and dryland conditions.
Operating costs associated with commonly used crop production practices
including fertilizer, herbicide, seed, insecticide, fuel, irrigation
technology maintenance, irrigation labor, and harvesting costs are
calculated. Finally, hydrologic data including the area of each county
overlying the aquifer, number of wells, and total crop acres per
irrigation well, average saturated thickness of the aquifer, initial
well yield, and average pump lift are collected for each county.
An estimated specific yield of 0.15 is used for the entire study
area and the initial well yield by county is estimated using the methods
described in the analytical study of the Ogallala aquifer in various
counties. The southern portion of the Ogallala aquifer has no
significant recharge. Hence, it is assumed for modeling purposes that
there is no recharge of the aquifer occurring in the study area. The
number of acres irrigated using groundwater and the number of wells in
each county are obtained from the state reports available at the NASS
website.
The GAMS models also include county-specific data such as aquifer
recharge rate, acres planted in each crop and system in the base year,
budgeted 2007 production and irrigation costs, actual 2007 crop prices,
and a three-year average dryland yield as reported by the National
Agricultural Statistics Service (NASS).
The specific policy models also include constraints for water
usage, crop substitution, and dryland substitution, as well as revenue,
cost, and hydrologic calculations. Saturated thickness values for each
county were obtained from the Texas Tech University Center for
Geospatial Technology, with the initial (2004) average saturated
thickness and were used as the beginning saturated thickness for each
county in the baseline and policy GAMS models.
These models were run optimizing the net present value of profits
over a sixty year horizon, providing detailed results showing changes in
the average saturated thickness of the aquifer, net present value for
returns, the amount of water use, and the acreage planted under each
crop and system (dry land or irrigated) for each county for each of the
sixty years modeled. The baseline scenario assumes that no water
conserving policy is implemented and producers operate in an unregulated
profit maximizing manner. The only restrictions in the models for the
target area are a maximum of 36 inches of irrigation allowed per crop
per year, the maximum annual withdraw cannot exceed the actual 2005-07
average level of water use as reported by the Texas Water Development
Board, and the saturated thickness is not allowed to fall below 20 feet.
The specific conservation scenarios were selected based on a survey
by The Economics Section of the Ogallala Aquifer Project, and include
the adoption of biotechnology resulting in a 1% decrease in water use
while providing a 0.5% increase in yields, the adoption of irrigation
technology where 1% of irrigated cropland is converted to drip
irrigation until a total of 10% is reached, a mandatory water use
restriction reducing water use by 1 %,the temporary conversion (TCD) of
10% of irrigated acreage to dryland production for 15 years, and the
permanent conversion (PCD) of 10% of irrigated acreage to dryland
production.
Model Specification: In order to estimate the economic life of the
aquifer across the region, a dynamic optimization model is developed.
The objective of the study is to maximize the net returns from crop
production over a sixty-year planning period for a given county as a
whole. The objective function is:
Max NPV = [60.summation over (t=1)] [NR.sub.t][(1 + r).sup.-t] (i)
Where, NPV is the net present value of net returns, r is the
discount rate, and [NR.sub.t] is the net revenue at time t. [NR.sub.t]
is defined as:
[NR.sub.t] = [[summation].sub.i] [[summation].sub.k]
[[THETA].sub.ikt] {[P.sub.i] [Y.sub.ikt] [[WA.sub.ikt],([WP.sub.ikt])] -
[C.sub.ik]([WP.sub.ikt], [X.sub.t], [ST.sub.t])} (ii)
where i represents crop grown, k represents irrigation methods
used, [[THETA].sub.ikt] is the percentage of crop i produced using
method k in time t, [P.sub.i] is the output price of the crop i,
[WA.sub.ikt] water applied per acre, [WP.sub.ikt] water pumped per acre,
[Y.sub.ikt] is the per acre yield production function, [C.sub.ik]
represents costs per acre, [X.sub.t] is the pump lift at time t, and
[ST.sub.t] is the saturated thickness of the aquifer at time t.
The constraints of the model are:
[ST.sub.t+1]= [ST.sub.t] - [([[summation].sub.i],
[[summation].sub.k] [[THETA].sub.ikt] * [WP.sub.ikt])]A/s, (iii)
[X.sub.t+1] = [X.sub.t] + [([[summation].sub.i],
[[summation].sub.k] [[THETA].sub.ikt] * [WP.sub.ikt])]A/s, (iv)
[GPC.sub.t] = [([ST.sub.t]/IST).sup.2] * (4.42*WY/AW), (v)
[WT.sub.t] = [[summation].sub.i] [[summation].sub.k]
[[THETA].sub.ikt] * [WP.sub.ikt], (vi)
[WT.sub.t] [less than or equal to] [GPC.sub.t] (vii)
[PC.sub.ikt] = {[EF([X.sub.t] + 2.31 * PSI)EP]/EFF} * [WP.sub.ikt],
(viii)
[C.sub.ikt] = [VC.sub.ik] + [PC.sub.ikt] + [HC.sub.ikt] +
[MC.sub.k] + [DP.sub.k] + [LC.sub.k] (ix)
[[summation].sub.i] [[summation].sub.k] [[THETA].sub.ikt] [less
than or equal to] 1 for all t, (x)
[[THETA].sub.ikt] [greater than or equal to] (0.9)
[[THETA].sub.ikt-1] (xi)
[[THETA].sub.ikt] [greater than or equal to] 0. (xii)
Equation (iii) updates the saturated thickness variable and
equation (iv) updates the pumping lift variable in the model. A is the
percentage of irrigated acres expressed as the initial number of
irrigated acres in the county divided by the area of the county
overlying the aquifer, and s is the specific yield of the aquifer. GPC
in equation (v) is the gross pumping capacity, IST represents the
initial saturated thickness of the aquifer and WY represents the average
initial well yield for the county. The factor 4.42 assumes 2000 hours of
pumping per season and has the units AcIn/GPM. Thus, GPC unit is
AcIn/GPM. Equation (vi) represents the total amount of water pumped per
acre, [WT.sub.t], is the average water use on all acreage. Constraint
(vii) requires [WT.sub.t] to be less than or equal to GPC.
Equations (viii) and (ix) represent the cost functions in the
model. In Equation (viii), [PC.sub.ikt] represents the cost of pumping;
EF represents the energy use factor for natural gas, EP is the price of
natural gas, EFF represents pump efficiency, and 2.31 feet is the height
of a column of water that will exert a pressure of 1 pound per square
inch. Equation (ix) represents the cost of production, [C.sub.ikt] in
terms of [VC.sub.ik], is the variable cost of production per acre,
[HC.sub.ikt], the harvest cost per acre, [MC.sub.k], the irrigation
system maintenance cost per acre, [DP.sub.k], the per acre depreciation
of the irrigation system per year, and [LC.sub.k], the cost of labor per
acre for the irrigation system. Equation (x) limits the sum of all acres
of crops i produced by irrigation systems k for time period t to be less
than or equal to one (1). Equation (xi) is a constraint placed in the
model to limit the annual shift to a 10% change from the previous
year's acreage. Equation (xii) is a non-negativity constraint to
assure all decision variables in the model take on positive values.
Results and Discussion: A survey of stakeholders identified five
strategies to be analyzed: permanent conversion to dryland production,
technology adoption, biotechnology, water use restriction, and temporary
conversion to dryland production. Economic optimization models were
developed to estimate changes in the aquifer, irrigated acreage and net
farm income over a 60 year planning period. Socioeconomic models were
utilized to evaluate impacts on the regional economy. Each conservation
strategy was then evaluated with respect to the change in saturated
thickness, producer income and impacts on the regional economy relative
to the baseline.
The study region has a population of 388,971, average income per
household of $60,682 and covers 23,292 square miles. The economy of the
Texas Panhandle region is comprised of total industry output of $23
billion, value added of $10 billion, and employment of 203,689. The
target area includes Castro, Dallam, Deaf Smith, Hartley, Moore, Parmer,
Sherman, and Swisher counties in the Texas Panhandle. Dallam, Hartley,
Moore and Sherman counties are located in the Texas Water Development
Board's (TWDB) Groundwater Management Area 1 (GMA1), and are all
part of the North Plains Groundwater Conservation District. Castro, Deaf
Smith, Parmer, and Swisher counties are in the TWDB Groundwater
Management Area 2 (GMA2), with Deaf Smith, Parmer, and Castro counties
being located in the High Plains Underground Water Conservation District
No. 1. The target area consists of 2,398,567 cropland acres, of which
approximately 63% are irrigated. These eight counties consume
approximately 2.3 million acre-feet of groundwater annually. The
saturated thickness of the aquifer in this area averages 110 feet, and
ranges from approximately 43 feet in Swisher County to approximately 182
feet in Sherman County. Approximately 95% of all irrigated acres in the
area are under center pivot sprinkler irrigation systems. Of the total
irrigated acres under all practices, approximately 38% is planted in
sprinkler-irrigated corn, 30% in sprinkler-irrigated wheat, and 16% in
sprinkler-irrigated cotton.
Results of the alternative water conservation policy scenarios were
compared to the baseline scenario to identify the relative effect of the
policy. The results of this analysis will provide the primary
information that policy makers and state agencies need to assess the
potential economic implications of these policy alternatives.
Baseline Scenario
The baseline scenario assumes that no water conserving policy is
implemented and producers operate in an unregulated profit maximizing
manner. Under these assumptions, on average, over the 60 year planning
horizon the saturated thickness declines to 43.7 feet (Table 1). As
saturated thickness declines, well capacity diminishes and pumping costs
increase which results in total annual water use being reduced from
2,303,317 acre-feet to 754,794 acre-feet (Table 2). When water use is
restricted, producers of irrigated crops respond by reducing total
irrigated acres, shifting to a less water intensive crop, or converting
to dryland production. As this occurs, the percent of irrigated acreage
within the target area declines to 17.4% (Table 3). The net effect of
this scenario is that the target area average net income per acre is
reduced approximately 40.8% to $106.85 per acre (Table 4). Over the 60
year planning horizon, each cropland acre generates a net present value
of $4,307.36.
The socioeconomic impacts of agricultural crop production in the
region are presented in 2007 dollars (Table 5). Gross receipts of
$47,622 million from crop production result in a total economic impact
of $105,970 million in industry output, $48,634 million in value added
and an average of 29,183 jobs over 60 years.
Biotechnology Adoption Scenario
The biotechnology adoption scenario assumes that water use is
reduced at the rate of 1% per year, and crop yields increase at the rate
of 0.5% per year. Under these assumptions, on average, over the 60 year
planning horizon the saturated thickness declines to 49.2 feet leaving
approximately 12.4% more saturated thickness by year 60 than the
baseline scenario (Table 1). As saturated thickness declines, well
capacity diminishes, pumping costs increase, and it takes less water to
reach the profit maximizing level which results in total annual water
use being reduced from 2,303,317 acre-feet to 588,155 acre-feet or
approximately 22.1% less than the baseline scenario (Table 2). When
water use is restricted, producers of irrigated crops respond by
reducing total irrigated acres, shifting to a less water intensive crop,
or converting to dryland production. As this occurs, the percent of
irrigated acreage within the target area declines to 14.9% or 14.4% less
than the baseline scenario (Table 3). The net effect of this scenario is
that the target area average net income per acre increases over time to
$225.77 per acre or 111.3% more than the baseline scenario (Table 4).
Over the 60 year planning horizon, each cropland acre generates a net
present value of $5,505.16 or 27.8% more than the baseline scenario.
The socioeconomic impacts of agricultural crop production in the
region under the biotechnology scenario are approximately 6% higher than
the baseline scenario over 60 years (Table 5). Gross receipts of $50,243
million from crop production result in a total economic impact of
$111,993 million in industry output, $51,337 million in value added and
an average of 30,434 jobs.
Irrigation Technology Adoption Scenario
The irrigation technology adoption scenario assumes that irrigation
efficiency improves as LEPA style center pivots (95% efficient) are
replaced by sub-surface drip systems (99% efficient) until 10% of the
irrigated acreage is irrigated with sub-surface drip technology. Under
these assumptions, on average, over the 60 year planning horizon the
saturated thickness declines to 43.7 feet or approximately no change
from the baseline scenario (Table 1). As saturated thickness declines,
well capacity diminishes, pumping costs increase, and it takes less
water to reach the profit maximizing level which results in total annual
water use being reduced from 2,303,317 acre-feet to 754,609 acre-feet or
approximately no change from the baseline scenario (Table 2). When per
acre water use is restricted, producers of irrigated crops respond by
reducing total irrigated acres, shifting to a less water intensive crop,
or converting to dryland production. As this occurs, the percent of
irrigated acreage within the target area declines to 17.3% or 0.3% less
than the baseline scenario (Table 3). The net effect of this scenario is
that the target area average net income per acre decreases over time to
$105.73 per acre or 1.1% less than the baseline scenario (Table 4). Over
the 60 year planning horizon, each cropland acre generates a net present
value of $4,216.14 or 2.1% less than the baseline scenario.
There is very little change in socioeconomic impacts of
agricultural crop production in the region under the technology adoption
scenario compared to the baseline scenario over 60 years (Table 5).
Gross receipts of $47,410 million from crop production result in a total
economic impact of $105,509 million in industry output, $48,430 million
in value added and an average of 29,026 jobs.
Water Use Restriction Scenario
The water use restriction scenario assumes that water use is
reduced at the rate of 1% per year. Under this assumption, on average,
over the 60 year planning horizon the saturated thickness declines to
49.2 feet or approximately 12.4% more than the baseline scenario (Table
1). As saturated thickness declines, well capacity diminishes, pumping
costs increase, and it takes less water to reach the profit maximizing
level which results in total annual water use being reduced to 596,510
acre-feet or approximately 21.0% less than the baseline scenario (Table
2). When per acre water use is restricted, producers of irrigated crops
respond by reducing total irrigated acres, shifting to a less water
intensive crop, or converting to dryland production. As this occurs, the
percent of irrigated acreage within the target area declines to 14.0% or
19.6% less than the baseline scenario (Table 3). The net effect of this
scenario is that the target area average net income per acre decreases
over time to $99.43 per acre or 6.9% less than the baseline scenario
(Table 4). Over the 60 year planning horizon, each cropland acre
generates a net present value of $4,074.99 or 5.4% less than the
baseline scenario.
The socioeconomic impacts of agricultural crop production in the
region under the water use restriction scenario are approximately three
percent lower than the baseline scenario over 60 years (Table 5). Gross
receipts of $46,249 million from crop production result in a total
economic impact of $103,014 million in industry output, $47,273 million
in value added and an average of 28,133 jobs.
Temporary Conversion to Dryland Scenario
The temporary conversion to dryland scenario assumes that 2% of the
initial irrigated acreage is converted to dryland use each year for 5
years for a total of 10%. This acreage is then allowed to re-enter
irrigated production after year 15. Under these assumptions, on average,
over the 60 year planning horizon the saturated thickness declines to
44.1 feet or approximately 0.8% more than the baseline scenario (Table
1). As saturated thickness declines, well capacity diminishes, pumping
costs increase, and it takes less water to reach the profit maximizing
level which results in total annual water use being reduced to 764,236
acre-feet or approximately 1.3% more than the baseline scenario (Table
2). When per acre water use is restricted, producers of irrigated crops
respond by reducing total irrigated acres, shifting to a less water
intensive crop, or converting to dryland production. As this occurs, the
percent of irrigated acreage within the target area declines to 17.6% or
1.4% more than the baseline scenario (Table 3). The net effect of this
scenario is that the target area average net income per acre decreases
over time to $107.21 per acre or 0.3% more than the baseline scenario
(Table 4). Over the 60 year planning horizon, each cropland acre
generates a net present value of $4,197.53 or 2.5% less than the
baseline scenario.
The socioeconomic impacts of agricultural crop production in the
region under the temporary conversion to dryland scenario are two
percent lower than the baseline scenario over 60 years (Table 5). Gross
receipts of $46,764 million from crop production result in a total
economic impact of $104,069 million in industry output, $47,765 million
in value added and an average of 28,637 jobs.
Permanent Conversion to Dryland Scenario Plan A
This permanent conversion to dryland scenario assumes that 2% of
irrigated acreage is idled each year for the first 5 years for a total
of 10%. This acreage then remains idled for 15 years and is then allowed
to resume the production of dryland crops. Under these assumptions, on
average, over the 60 year planning horizon the saturated thickness
declines to 44.2 feet or approximately 1.1% more than the baseline
scenario (Table 1). As saturated thickness declines, well capacity
diminishes, pumping costs increase, and it takes less water to reach the
profit maximizing level which results in total annual water use being
reduced to 768,282 acre-feet or approximately 1.8% more than the
baseline scenario (Table 2). When per acre water use is restricted,
producers of irrigated crops respond by reducing total irrigated acres,
shifting to a less water intensive crop, or converting to dryland
production. As this occurs, the percent of irrigated acreage within the
target area declines to 17.7% or 1.9% more than the baseline scenario
(Table 3). The net effect of this scenario is that the target area
average net income per acre decreases over time to $107.36 per acre or
0.5% more than the baseline scenario (Table 4). Over the 60 year
planning horizon, each cropland acre generates a net present value of
$4,187.06 or 2.8% less than the baseline scenario.
The socioeconomic impacts of agricultural crop production in the
region under the permanent conversion to dryland scenario are two
percent lower than the baseline scenario over 60 years (Table 5). Gross
receipts of $46,650 million from crop production result in a total
economic impact of $103,813 million in industry output, $47,658 million
in value added and an average of 28,564 jobs.
Permanent Conversion to Dryland Scenario Plan B
This permanent conversion to dryland scenario assumes that 2% of
irrigated acreage is converted to dryland production each year for the
first 5 years for a total of 10%. This acreage is allowed to immediately
convert to the production of dryland crops. Under these assumptions, on
average, over the 60 year planning horizon the saturated thickness
declines to 44.2 feet or approximately 1.1% more than the baseline
scenario (Table 1). As saturated thickness declines, well capacity
diminishes, pumping costs increase, and it takes less water to reach the
profit maximizing level which results in total annual water use being
reduced to 768,282 acre-feet or approximately 1.8% more than the
baseline scenario (Table 2). When per acre water use is restricted,
producers of irrigated crops respond by reducing total irrigated acres,
shifting to a less water intensive crop, or converting to dryland
production. As this occurs, the percent of irrigated acreage within the
target area declines to 17.7% or 1.9% more than the baseline scenario
(Table 3). The net effect of this scenario is that the target area
average net income per acre decreases over time to $107.36 per acre or
0.5% more than the baseline scenario (Table 4). Over the 60 year
planning horizon, each cropland acre generates a net present value of
$4,244.23 or 1.5% less than the baseline scenario.
The socioeconomic impacts of agricultural crop production in the
region under the permanent conversion to dryland scenario are one
percent lower than the baseline scenario over 60 years (Table 5). Gross
receipts of $47,013 million from crop production result in a total
economic impact of $104,619 million in industry output, $48,052 million
in value added and an average of 28,773 jobs.
Conclusion: The policies that showed the best results in terms of
conserving the water available in the Ogallala Aquifer were the
biotechnology adoption scenario and the water use restriction scenario.
Both of these policies assume a 1% reduction in water use per year
during the 60-year planning horizon. The irrigation adoption scenario
resulted in very little savings of water, but it did allow for a greater
number of irrigated acres in the later years of the scenario due to a
slight decrease in water use per acre resulting from improved irrigation
efficiency. Finally, the temporary and permanent conversion to dryland
scenarios did not show any change in terms of water use compared to the
baseline. This was due to the fact that conversion to dryland in the
baseline occurs at about the same rate as that specified in the
conversion policies.
In terms of economic costs, the biotechnology adoption policy by
far provides the greatest net returns and net present values. However,
as was previously mentioned, the yield increases provided in the models
are based on seed varieties that are not yet available to producers. The
next best policy in terms of net present value of returns was the
irrigation adoption technology in that, while it decreased from the
baseline, it only decreased by 0.31%. The conversion to dryland policies
each had an increased net present value over the baseline, although this
was because of the time of the net returns. Finally, the water-use
restriction policy cost most of all the policies, resulted in a net
present value per acre 5.48% less than the baseline as well as
significantly fewer net returns throughout the 60-year simulation.
Several implications can be derived from the results of this study.
First, some form of long term water use restriction (percentage per year
or permanent conversion) is necessary in order to achieve any meaningful
water savings. Second, accelerated adoption of improved biotechnology or
irrigation technology without restrictions will not save water and, in
fact, could increase water use lowering water availability in the
future. However, using these strategies in combination with a water use
restriction policy can negate the negative impacts to producer income
and the regional economy. Finally, temporary conversion to dryland has
little impact on long term water savings and should not be pursued.
References:
Adusumilli, Naveen C. 2007. "Economic Optimization of Ogallala
Aquifer in the Oklahoma Panhandle" M.S. Thesis, West Texas A&M
University, Department of Agricultural Sciences, Canyon, Texas.
Amosson, S. H., Lal K. Almas, F. E. Bretz, DeDe Jones, Patrick
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(C1). Texas AgriLife Extension Service, Texas A&M University System,
College Station, Texas.
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Economics of Irrigation Systems." Texas Agricultural Extension
Bulletin B-6113, Texas Cooperative Extension, The Texas A&M
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Users Guide, GAMS Development Corporation, Washington, DC.
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Irrigated Agriculture." Agronomy Journal, 93, pp 281-289 (2001).
Park, Seong C. 2005. "Economic Optimization of Groundwater
Resources in the Texas Panhandle." M. S. Thesis, West Texas A&M
University, Division of Agriculture, Canyon, Texas.
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http://www.nass.usda.gov/census/ (accessed November 14, 2006).
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Published by the Forum on Public Policy
Lal K. Almas, Associate Professor of Agricultural Business and
Economics, Department of Agricultural Sciences, West Texas A&M
University
Robert H. Taylor, Department of Agricultural Sciences, West Texas
A&M University
Bridget Guerrero, Extension Associate, Texas AgriLife Extension
Service, Lubbock, Texas
Stephen H. Amosson, Regents Fellow Professor, Extension Economist,
Texas AgriLife Extension Service, Amarillo, TX
Table 1. Central Sub-Region Target Area Weighted Average
Saturated Thickness (feet) *
Policy Scenario Year 0 Year 10 Year 20 Year 30
Baseline 111.3 95.5 78.1 65.0
Biotechnology 111.3 96.1 81.7 70.1
Change 0.0% 0.7% 4.7% 7.9%
Irrigation Tech. 111.3 95.1 77.3 64.6
Change 0.0% -0.4% -1.0% -0.5%
Water Use Rest. 111.3 95.1 79.5 68.8
Change 0.0% -0.4% 1.9% 6.0%
Temporary Conv. 111.3 95.5 78.3 65.4
Change 0.0% 0.1% 0.3% 0.7%
Permanent Conv. (A) 111.3 95.5 78.3 65.6
Change 0.0% 0.0% 0.3% 1.1%
Permanent Conv. (B) 111.3 95.5 78.3 65.6
Change 0.0% 0.0% 0.3% 1.1%
Policy Scenario Year 40 Year 50 Year 60
Baseline 55.7 49.0 43.7
Biotechnology 60.8 54.0 49.2
Change 9.2% 10.2% 12.4%
Irrigation Tech. 55.7 49.0 43.7
Change -0.0% -0.0% -0.0%
Water Use Rest. 60.3 53.9 49.2
Change 8.3% 10.0% 12.4%
Temporary Conv. 56.3 49.5 44.1
Change 1.1% 0.9% 0.8%
Permanent Conv. (A) 56.5 49.7 44.2
Change 1.4% 1.2% 1.1%
Permanent Conv. (B) 56.5 49.7 44.2
Change 1.4% 1.2% 1.1%
* Averages are weighted by the area overlying the aquifer
in each county.
Table 2. Central Sub-Region Target Area Total Water Use
(1,000 acre-feet)
Policy Scenario Year 0 Year 10 Year 20 Year 30
Baseline 2,303 2,489 2,182 1,659
Biotechnology 2,303 2,151 1,744 1,201
Change 0.0% -13.6% -20.1% -27.6%
Irrigation Tech. 2,303 2,490 2,098 1,538
Change 0.0% 0.1% -3.8% -7.3%
Water Use Rest. 2,303 2,318 1,671 1,391
Change 0.0% -6.9% -23.4% -16.2%
Temporary Conv. 2,303 2,405 2,105 1,564
Change 0.0% -3.4% -3.5% -5.8%
Permanent Conv. (A) 2,303 2,412 2,040 1,550
Change 0.0% -3.1% -6.5% -6.6%
Permanent Conv. (B) 2,303 2,412 2,040 1,550
Change 0.0% -3.1% -6.5% -6.6%
Policy Scenario Year 40 Year 50 Year 60 Total
Baseline 1,121 899 755 98,446
Biotechnology 1,104 840 588 87,882
Change -1.5% -6.6% -22.1% -10.7%
Irrigation Tech. 1,120 899 755 96,935
Change 0.0% 0.0% 0.0% -1.5%
Water Use Rest. 1,046 815 597 87,881
Change -6.6% -9.4% -21.0% -10.7%
Temporary Conv. 1,141 913 764 96,507
Change 1.8% 1.5% 1.3% -2.0%
Permanent Conv. (A) 1,150 919 768 96,340
Change 2.6% 2.2% 1.8% -2.1%
Permanent Conv. (B) 1,150 919 768 96,340
Change 2.6% 2.2% 1.8% -2.1%
Table 3. Central Sub-Region Target Area Irrigated Acres as a
Percentage of Total Acres *
Policy Scenario Year 0 Year 10 Year 20 Year 30
Baseline 63.0% 56.8% 49.7% 38.1%
Biotechnology 63.0% 50.7% 42.5% 36.5%
Change 0.0% -10.6% -14.5% -4.2%
Irrigation Tech. 63.0% 56.6% 47.6% 35.0%
Change 0.0% -0.2% -4.1% -8.1%
Water Use Rest. 63.0% 53.5% 39.6% 33.0%
Change 0.0% -5.7% -20.2% -13.3%
Temporary Conv. 63.0% 54.1% 47.3% 35.9%
Change 0.0% -4.6% -4.8% -5.6%
Permanent Conv. (A) 63.0% 54.1% 45.2% 35.2%
Change 0.0% -4.6% -9.0% -7.4%
Permanent Conv. (B) 63.0% 54.1% 45.2% 35.2%
Change 0.0% -4.6% -9.0% -7.4%
Policy Scenario Year 40 Year 50 Year 60
Baseline 25.8% 20.7% 17.4%
Biotechnology 27.3% 20.9% 14.9%
Change 5.9% 1.1% -14.4%
Irrigation Tech. 25.7% 20.7% 17.3%
Change -0.4% -0.4% -0.3%
Water Use Rest. 24.6% 19.1% 14.0%
Change -4.7% -7.8% -19.6%
Temporary Conv. 26.3% 21.1% 17.6%
Change 2.0% 1.6% 1.4%
Permanent Conv. (A) 26.5% 21.2% 17.7%
Change 2.8% 2.3% 1.9%
Permanent Conv. (B) 26.5% 21.2% 17.7%
Change 2.8% 2.3% 1.9%
* The percentage is based on the total irrigated acres in the
target area (at time = t) divided by total irrigated and
nonirrigated cropland acres in the target area.
Table 4. Central Sub-Region Target Area Average Net Income
per Acre *
Policy Scenario Year 10 Year 20 Year 30 Year 40
Baseline $180.48 $165.10 $142.80 $119.52
Biotechnology $191.13 $197.58 $207.30 $208.67
Change 5.9% 19.7% 45.2% 74.6%
Irrigation Tech. $177.00 $158.91 $136.44 $117.73
Change -1.9% -3.8% -4.5% -1.5%
Water Use Rest. $172.13 $145.52 $132.87 $116.79
Change -4.6% -11.9% -7.0% -2.3%
Temporary Conv. $171.54 $161.43 $140.13 $120.32
Change -5.0% -2.2% -1.9% 0.7%
Permanent Conv. (A) $171.62 $158.47 $139.38 $120.69
Change -4.9% -4.0% -2.4% 1.0%
Permanent Conv. (B) $176.76 $159.50 $139.38 $120.69
Change -2.1% -3.4% -2.4% 1.0%
Net
Policy Scenario Year 50 Year 60 Present
Value
Baseline $111.78 $106.85 $4,307.36
Biotechnology $217.25 $225.77 $5,505.16
Change 94.4% 111.3% 27.8%
Irrigation Tech. $110.39 $105.73 $4,216.14
Change -1.2% -1.1% -2.1%
Water Use Rest. $107.69 $99.43 $4,074.99
Change -3.7% -6.9% -5.4%
Temporary Conv. $112.30 $107.21 $4,197.53
Change 0.5% 0.3% -2.5%
Permanent Conv. (A) $112.52 $107.36 $4,187.06
Change 0.7% 0.5% -2.8%
Permanent Conv. (B) $112.52 $107.36 $4,244.23
Change 0.7% 0.5% -1.5%
* The average is based on the total irrigated and nonirrigated
net revenue (at time = t) divided by total irrigated and
nonirrigated cropland acres.
Table 5. Texas Panhandle 60 Year Regional Economic Impacts
Direct Indirect Induced Total
Baseline
Output * 47,622 37,319 21,029 105,970
Value Added * 15,478 20,274 12,882 48,634
Employment 17,922 7,561 3,701 29,183
Biotech
Output * 50,243 39,437 22,313 111,993
Value Added * 16,097 21,572 13,668 51,337
Employment 18,327 8,180 3,927 30,434
Technology Adoption
Output * 47,410 37,144 20,955 105,509
Value Added * 15,398 20,195 12,837 48,430
Employment 17,792 7,547 3,688 29,026
Water Use Restriction
Output * 46,249 36,243 20,523 103,014
Value Added * 14,909 19,793 12,572 47,273
Employment 17,044 7,476 3,612 28,133
Temporary Conversion
Output * 46,764 36,641 20,664 104,069
Value Added * 15,188 19,919 12,658 47,765
Employment 17,560 7,440 3,636 28,637
Permanent Conversion (A)
Output * 46,650 36,541 20,623 103,813
Value Added * 15,156 19,869 12,633 47,658
Employment 17,508 7,427 3,629 28,564
Permanent Conversion (B)
Output * 47,013 36,799 20,806 104,619
Value Added * 15,282 20,025 12,745 48,052
Employment 17,611 7,500 3,662 28,773
Change %
from Change
Baseline from
Baseline
Baseline
Output *
Value Added *
Employment
Biotech
Output * 6,023 6%
Value Added * 2,704 6%
Employment 1,251 4%
Technology Adoption
Output * -462 0%
Value Added * -204 0%
Employment -157 -1%
Water Use Restriction
Output * -2,956 -3%
Value Added * -1,360 -3%
Employment -1,050 -4%
Temporary Conversion
Output * -1,902 -2%
Value Added * -869 -2%
Employment -546 -2%
Permanent Conversion (A)
Output * -2,157 -2%
Value Added * -976 -2%
Employment -619 -2%
Permanent Conversion (B)
Output * -1,352 -1%
Value Added * -582 -1%
Employment -411 -1%
* Millions of dollars
