The continued decline in the availability of water from the Ogallala Aquifer in the Texas Panhandle has led to an increased interest in conservation policies designed to extend the life of the aquifer and sustain rural economies. Four counties were chosen for evaluation. This study evaluates the effectiveness of five policies in terms of changes in the saturated thickness, crop mix, water use per acre, and the net present value of farm profits over a 60-year planning horizon. The dynamic optimization models were developed using GAMS for the baseline as well as one for all five of the policy alternatives for each county. Results indicate that the policy scenarios of biotechnology adoption and a water use restriction will conserve the most water among the policies analyzed. In terms of economic returns, the biotechnology adoption policy by far provides the greatest benefit to producers due to yield increases that are estimated with current annual growth rates in new seed varieties. The water use restriction policy, on the other hand, has the lowest net present value of returns, indicating that conservation is accompanied with significant costs to producers. The irrigation adoption technology scenario is the next best policy in terms of net present value of returns (following biotechnology); however, it ranks last in terms of reducing aquifer depletion. It is important to note that while the models do not perfectly predict the factors being evaluated, it is the basis for comparison between the policy scenarios which are important. These comparisons will aid policy makers in determining the most effective strategy to conserve water while simultaneously considering the economic costs to producers. In addition, the results of this study can be applied to other areas facing similar conditions, either currently or in the future, throughout the Texas Panhandle.
The availability of water in the Texas Panhandle is a major concern, as is the conservation of the limited supply of water in the region. The Texas High Plains area has a semi-arid climate and low average rainfalls, which results in little surface water being available year-round for agriculture. As a result, more than 90% of the water used in agriculture in the High Plains area comes from the Ogallala Aquifer [
It is estimated that in the year 2010, approximately 6,111,751 acre-feet of water was pumped in the Texas Panhandle for municipal, industrial, steam-electric power generation, mining, irrigation, and total livestock use. An estimated 5,793,933 acre-feet of water was used in the agricultural industry with 93.25% of the total water being used for irrigated crop production. Additionally, livestock operations consumed about 1.48% of the water used in the region [
The use of low-energy-precision-application (LEPA) and low-energy-spray- application (LESA) has allowed for more efficient use of water in the region [
The unique nature of the Aquifer, region, and Texas groundwater law present some interesting and confounding problems for water policymakers. Although the rule of capture continues to be the primary law for governing groundwater use in Texas, the management and regulation of groundwater use is carried out by underground water conservation districts (UWCD). These were established by the Fifty-First Texas Legislature in 1949, with the first district (High Plains Underground Water Conservation District No. 1) being created in the Texas Panhandle in 1951. In the current scenario, there are 98 groundwater conservation districts in Texas, of which 96 are confirmed and the remaining two have yet to be confirmed by voters via means of local elections [
Texas legislation, specifically Senate Bills 1 [
The main goal of any conservation policy is to limit the use of a resource in an effort to preserve the quantity of that resource for other purposes for future use [
The overall objective of the study is to provide policy makers and other interested individuals an analysis of the estimated impacts of alternative water conservation policies. The specific research objective of this study is the evaluation of a baseline and five alternative policies designed to conserve groundwater in the Dallam, Hartley, Moore, and Sherman Counties of the Texas Panhandle. These counties were chosen because they have shown a significant level of water depletion in the baseline scenario during the 60-year simulation. In addition, these counties have been projected in Senate Bills 1 [
Generalized Algebraic Modeling System (GAMS), a computer software optimization program, was used in the study to solve the optimization models formulated and to evaluate the water conservation policies [
The specific policy models also include constraints for water usage, crop substitution, and dry land 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 average saturated thickness for Dallam County being 128 feet, Hartley County 153 feet, Moore County 162 feet, and Sherman 182 feet (Barbato and Mulligan, 2009). These values were used as the beginning saturated thickness for each county in the baseline and policy GAMS models. Texas Water Development Board’s “Report 347” on irrigation in Texas was used to obtain the initial irrigated acreage data with the four-county area consisting of 1,027,167 cropland acres of which 807,008 acres are irrigated. Of these irrigated acres, 78.6% are irrigated with LEPA-style center pivot sprinkler systems. Dallam County consists of 278,067 cropland acres with 247,141 acres being irrigated of which 246,238 are irrigated with center pivot systems and the rest furrow irrigated. Hartley County includes 235,733 cropland acres including 187,169 irrigated acres with 185,169 under center pivot sprinkler, 2000 furrow, and 65 drip irrigation. In Moore County, there are 233,267 cropland acres with 143,787 acres under irrigation of which 128,725 are sprinkler irrigated and 15,062 are furrow irrigated. Sherman County has 280,100 cropland acres with 228,911 of these being irrigated, 217,931 sprinklers, 10,980 furrows, and 12 drips [
In order to estimate the economic life of the Aquifer across the region, a dynamic economic optimization model is used originally developed by [
where, NPV is the net present value of net returns (NR), r is the discount rate, and NRt is the net revenue at time t and NRt is defined as:
where, i represents crop grown, k represents irrigation methods used, Θikt is the percentage of crop i produced using irrigation method k in time t, Pi is the output price of the crop i, WAikt water applied per acre, WPikt water pumped per acre, Yikt is the per acre yield production function, Cik represents costs per acre, Xt is the pump lift at time t, and STt is the saturated thickness of the aquifer at time t. The constraints and or conditions/restrictions of the model are:
Equation (3) updates the saturated thickness variable and Equation (4) 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 the parameter specific yield, (SY), is the percentage of aquifer volume available for pumping. The SY value assigned to each county model is 0.15, which is the average value for the Southern Ogallala Aquifer. An equation of motion (Equation (4)) is used to monitor pumping-lift, which allows the model to capture the impact of agricultural water use on aquifer stock, pumping-lift, pumping cost, and net returns over the planning horizon.
GPC, gross pumping capacity, in Equation (5) expresses the amount of water available to be pumped as the gross pumping capacity, IST represents the initial saturated thickness of the aquifer, WY represents the average initial well yield for the county expressed in gallons per minute, AW is number of average acres served per well and 4.42 is a conversion factor to calculate GPC assuming 2000 hours of pumping in a growing season. Equation (6) represents the total amount of water pumped per acre, WTt, which is the average water use on all acreage. Constraint (7) requires that total water pumped, WTt has to be less than or equal to GPC.
Equations (8) and (9) represent the cost functions in the model. In Equation (8), PCikt 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 one pound per square inch. Equation (9) represents the cost of production, Cikt in terms of VCik, is the variable cost of production per acre, HCikt, the harvest cost per acre, MCk, the irrigation system maintenance cost per acre, DPk is the per acre depreciation of the irrigation system per year, and LCk is the cost of labor per acre for the irrigation system. Equation (10) 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 (11) is a constraint placed in the model to limit the annual shift to a 10% change from the previous year’s acreage. This limit on the rate of transition between crop enterprises attempts to control the rate at which the model switches from one enterprise to another to replicate an orderly transition between crop enterprises. Equation (12) is a non-negativity constraint to assure all decision variables in the model take on positive values. The discount rate of 3% was used to calculate the discounted farm returns. This rate was assumed in the light of long run expected rate of return on investment in farming business.
These simulation models were run optimizing the net present value of profits over a 60-year horizon, providing detailed results showing changes in the average saturated thickness of the aquifer, net present value for returns, the level of water use, and the acreage planted under each crop and system (dry land or irrigated) for each county for each of the 60-year modeled.
There have been several studies conducted in the state of Texas that focused on alternative policies to conserve groundwater. Reference [
i. Permanent conversion to dry land production: a voluntary incentive-based program that compensates landowners to permanently convert irrigated cropland to dry land (water right buyout),
ii. Irrigation technology adoption: a voluntary incentive based program that encourages land owners to adopt more water-efficient irrigation technology,
iii. Biotechnology: a voluntary incentive based program that encourages landowners to adopt more water-efficient, drought and insect resistant crop varieties,
iv. Water use restriction―a mandatory annual or multiyear limit that reduces the amount of water pumped, and
v. Temporary conversion to dry land production―a voluntary incentive-ba- sed program that compensates landowners to temporarily (10 years) converts irrigated cropland to dry land (water CRP).
The results of each policy scenario are intended to be used in the consideration of water conservation strategies in the future to ensure any strategy implemented minimizes detrimental effects on producer income and the economy while conserving water for future purposes. 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: 1) a maximum of 36 inches of irrigation is allowed per crop per year and 2) the saturated thickness is not allowed to fall below 20 feet. The biotechnology adoption scenario assumes that drought resistant crops are used, resulting in a 1% decline in water use each year while crop yields increase by 1.67% each year during the 60-year simulation. The most recent ERS estimated rate of growth in agricultural output from 1948-2006 is 1.67% [
The water use restriction scenario assumes that water use is reduced by 1% each year during the 60-year planning horizon. In the temporary conversion to dry land scenario, the assumption is that 2% of irrigated acreage is switched to dry land production each year for the first 5 years for a total of 10% by year 5. This acreage is then allowed to re-enter irrigated production after year 15 of the scenario. Finally, the permanent conversion to dry land scenario assumes that 2% of irrigated acreage is switched to dry land production each year for the first 5 years for a total of 10% by year 5. This acreage remains in dry land production for the remainder of the 60-year simulation.
The results from the baseline and each policy alternative were then compared to evaluate the effectiveness of each policy in conserving water in terms of reduced Aquifer withdrawals and water usage, the change in crop mix (irrigated versus dry land acreage), and the economic implications of each policy in terms of net present returns per acre for the four counties in this study.
The beginning regional average saturated thickness was 152.3 feet, with Dallam County having a thickness of 128 feet, Hartley 153 feet, Moore 162 feet, and Sherman 192 feet. In the unrestrained baseline scenario, the regional average saturated thickness declines 53.4% during the 60-year planning horizon to reach a level of 61.8 (
As the water level declines, well capacity drops and irrigation costs rise, leading to less water being required to reach a profit maximizing level of water use. As the per acre water use is decreased, producers shift production from water intensive crops (corn) to crops that require less water (sorghum) or to dry land crops. In the baseline scenario, the regional average water use per irrigated acre dropped from 25.3 acre-inches to 20.6 acre-inches by year 60 (
Policy Scenario: | Year 10 | Year 20 | Year 30 | Year 40 | Year 50 | Year 60 |
---|---|---|---|---|---|---|
Baseline | 132.85 | 111.42 | 92.77 | 79.35 | 69.45 | 61.84 |
Biotechnology- | 136.65 | 121.04 | 107.26 | 95.31 | 85.20 | 76.93 |
Change from Baseline | 2.86% | 8.63% | 15.62% | 20.11% | 22.67% | 24.39% |
Irrigation Technology- | 136.00 | 117.90 | 99.85 | 84.52 | 73.34 | 64.87 |
Change from Baseline | 2.37% | 5.81% | 7.63% | 6.51% | 5.59% | 4.90% |
Water Use Restriction- | 136.65 | 121.04 | 107.26 | 95.31 | 85.20 | 76.93 |
Change from Baseline | 2.86% | 8.63% | 15.62% | 20.11% | 22.67% | 24.39% |
Temporary Conversion- | 135.99 | 117.90 | 99.84 | 84.51 | 73.34 | 64.87 |
Change from Baseline | 2.37% | 5.81% | 7.63% | 6.51% | 5.59% | 4.90% |
Permanent Conversion- | 135.99 | 117.90 | 99.91 | 84.56 | 73.37 | 64.90 |
Change from Baseline | 2.37% | 5.81% | 7.70% | 6.57% | 5.64% | 4.94% |
Averages are weighted by the area overlying the aquifer in the region.
Policy Scenario: | Dallam | Hartley | Moore | Sherman | ||||
---|---|---|---|---|---|---|---|---|
Year 1 | Year 60 | Year 1 | Year 60 | Year 1 | Year 60 | Year 1 | Year 60 | |
Baseline | 128.0 | 49.2 | 153.0 | 73.2 | 162.0 | 55.8 | 182.0 | 70.6 |
Biotechnology | 128.0 | 61.5 | 153.0 | 89.9 | 162.0 | 65.7 | 182.0 | 92.7 |
Change from Baseline | 24.9% | 22.7% | 17.6% | 31.2% | ||||
Irrigation Technology | 128.0 | 52.2 | 153.0 | 76.5 | 162.0 | 57.6 | 182.0 | 74.6 |
Change from Baseline | 6.0% | 4.5% | 3.2% | 5.6% | ||||
Water Use Restriction | 128.0 | 61.5 | 153.0 | 89.9 | 162.0 | 65.7 | 182.0 | 92.7 |
Change from Baseline | 24.9% | 22.7% | 17.6% | 31.2% | ||||
Temporary Conversion | 128.0 | 52.2 | 153.0 | 76.5 | 162.0 | 57.6 | 182.0 | 74.6 |
Change from Baseline | 6.0% | 4.5% | 3.2% | 5.6% | ||||
Permanent Conversion | 128.0 | 52.2 | 153.0 | 76.5 | 162.0 | 57.7 | 182.0 | 74.6 |
Change from Baseline | 6.0% | 4.5% | 3.4% | 5.6% |
Policy Scenario: | Year 10 | Year 20 | Year 30 | Year 40 | Year 50 | Year 60 |
---|---|---|---|---|---|---|
Baseline | 24.7 | 23.7 | 20.8 | 20.7 | 20.6 | 20.6 |
Biotechnology- | 18.9 | 17.0 | 16.3 | 15.9 | 15.4 | 15.1 |
Change from Baseline | −23.52% | −28.30% | −21.52% | −23.35% | −25.32% | −26.79% |
Irrigation Technology- | 21.6 | 21.6 | 21.1 | 21.0 | 20.9 | 20.8 |
Change from Baseline | −12.78% | −9.16% | 1.49% | 1.23% | 1.25% | 1.27% |
Water Use Restriction- | 20.6 | 20.7 | 20.7 | 20.7 | 20.7 | 20.7 |
Change from Baseline | −16.74% | −12.69% | −0.41% | 0.06% | 0.38% | 0.59% |
Temporary Conversion- | 23.1 | 21.4 | 20.8 | 20.7 | 20.6 | 20.5 |
Change from Baseline | −6.77% | −9.70% | 0.04% | −0.16% | −0.15% | −0.13% |
Permanent Conversion- | 23.1 | 23.0 | 22.2 | 20.7 | 20.6 | 20.5 |
Change from Baseline | −6.77% | −3.10% | 6.69% | −0.13% | −0.12% | −0.12% |
The average is based on the total water use (at time = t) divided by the total irrigated acres (at time = t) for the region.
of all crop acres under irrigation to 27.5% of all acres in year 60, Hartley from 84.2% to 36.6%, Moore 59.2% to 19.7%, and Sherman from 63.4% to 25.5% (
The regional average net income per acre drops 48% from $191.26 to $100.30 per acre as producers shift their production away from irrigated crops (
In the biotechnology adoption scenario, the regional average saturated thickness drops 49.7% to 76.9 feet in year 60 of the simulation, which is 24.4% higher than the baseline scenario level (
Policy Scenario: | Year 10 | Year 20 | Year 30 | Year 40 | Year 50 | Year 60 |
---|---|---|---|---|---|---|
Baseline | 72.10% | 72.10% | 62.07% | 45.15% | 34.46% | 27.25% |
Biotechnology- | 71.99% | 71.20% | 65.03% | 57.57% | 49.65% | 40.86% |
Change from Baseline | −0.15% | −1.24% | 4.77% | 27.50% | 44.07% | 49.93% |
Irrigation Technology- | 69.35% | 69.35% | 68.04% | 50.59% | 37.92% | 29.59% |
Change from Baseline | −3.82% | −3.82% | 9.62% | 12.04% | 10.05% | 8.57% |
Water Use Restriction- | 66.07% | 58.40% | 51.20% | 44.00% | 36.81% | 29.62% |
Change from Baseline | −8.36% | −19.00% | −17.52% | −2.54% | 6.82% | 8.67% |
Temporary Conversion- | 64.89% | 69.80% | 69.08% | 51.31% | 38.48% | 30.02% |
Change from Baseline | −10.00% | −3.19% | 11.28% | 13.65% | 11.65% | 10.15% |
Permanent Conversion- | 64.89% | 64.89% | 64.89% | 51.37% | 38.51% | 30.04% |
Change from Baseline | −10.00% | −10.00% | 4.53% | 13.78% | 11.75% | 10.24% |
The percentage is based on the total irrigated acres (at time = t) divided by total irrigated and non-irrigated cropland acres in the region.
Policy Scenario: | Dallam | Hartley | Moore | Sherman | ||||
---|---|---|---|---|---|---|---|---|
Year 1 | Year 60 | Year 1 | Year 60 | Year 1 | Year 60 | Year 1 | Year 60 | |
Baseline | 81.4% | 27.5% | 84.2% | 36.6% | 59.2% | 19.7% | 63.4% | 25.5% |
Biotechnology | 81.4% | 41.1% | 84.2% | 44.9% | 59.2% | 33.4% | 63.4% | 43.5% |
Change from Baseline | 49.4% | 22.7% | 69.7% | 70.6% | ||||
Irrigation Technology | 81.4% | 29.6% | 84.2% | 39.5% | 59.2% | 21.0% | 63.4% | 28.4% |
Change from Baseline | 7.8% | 7.9% | 6.5% | 11.6% | ||||
Water Use Restriction | 81.4% | 32.4% | 84.2% | 33.3% | 59.2% | 26.3% | 63.4% | 26.6% |
Change from Baseline | 17.7% | −9.1% | 33.8% | 4.3% | ||||
Temporary Conversion | 81.4% | 30.8% | 84.2% | 39.9% | 59.2% | 21.0% | 63.4% | 28.4% |
Change from Baseline | 12.3% | 9.1% | 6.5% | 11.5% | ||||
Permanent Conversion | 81.4% | 30.8% | 84.2% | 39.9% | 59.2% | 21.1% | 63.4% | 28.4% |
Change from Baseline | 12.3% | 9.1% | 7.0% | 11.5% |
declines 52.0% to reach a level of 61.5 feet, Hartley declines 41.3% to 89.9 feet, Moore 59.5% to 65.7 feet, and Sherman 49.1% to 92.7 feet (
Irrigated acres as a percent of all cropland acres in this scenario increases above the baseline in year 60 by 49.93% to reach 40.86% of all acres (
Policy Scenario: | Year 10 | Year 20 | Year 30 | Year 40 | Year 50 | Year 60 |
---|---|---|---|---|---|---|
Baseline | $183.99 | $174.44 | $147.16 | $123.32 | $109.27 | $100.30 |
Biotechnology- | $252.02 | $336.47 | $422.27 | $507.26 | $588.28 | $661.21 |
Change from Baseline | 36.97% | 92.88% | 186.95% | 311.33% | 438.36% | 559.25% |
Irrigation Technology- | $165.60 | $160.19 | $151.63 | $127.07 | $111.00 | $101.04 |
Change from Baseline | −10.00% | −8.17% | 3.04% | 3.04% | 1.58% | 0.74% |
Water Use Restriction- | $159.82 | $146.99 | $134.46 | $123.07 | $112.67 | $103.11 |
Change from Baseline | −13.14% | −15.74% | −8.63% | −0.21% | 3.11% | 2.80% |
Temporary Conversion- | $164.66 | $165.23 | $157.62 | $131.42 | $114.24 | $103.56 |
Change from Baseline | −10.51% | −5.28% | 7.11% | 6.57% | 4.54% | 3.25% |
Permanent Conversion- | $164.66 | $163.21 | $156.75 | $131.53 | $114.30 | $103.59 |
Change from Baseline | −10.51% | −6.44% | 6.52% | 6.65% | 4.60% | 3.29% |
The average is based on the total irrigated and non-irrigated net revenue (at time = t) divided by total irrigated and non-irrigated cropland acres in the region.
Policy Scenario: | Dallam | Hartley | Moore | Sherman | Weighted Average |
---|---|---|---|---|---|
Baseline | $3907.96 | $4109.79 | $5825.06 | $4483.07 | $4546.47 |
Biotechnology- | $9158.70 | $9630.70 | $12062.27 | $9356.41 | $9980.33 |
Change from Baseline | 134.36% | 134.34% | 107.08% | 108.71% | 119.52% |
Irrigation Technology- | $3491.58 | $3899.80 | $5643.84 | $4252.84 | $4281.63 |
Change from Baseline | −10.65% | −5.11% | −3.11% | −5.14% | −5.83% |
Water Use Restriction- | $3447.58 | $3563.20 | $5398.98 | $3844.06 | $4025.39 |
Change from Baseline | −11.78% | −13.30% | −7.31% | −14.25% | −11.46% |
Temporary Conversion- | $3709.18 | $3924.80 | $5656.85 | $4305.89 | $4363.69 |
Change from Baseline | −5.09% | −4.50% | −2.89% | −3.95% | −4.02% |
Permanent Conversion- | $3704.73 | $3921.66 | $5629.04 | $4285.24 | $4349.82 |
Change from Baseline | −5.20% | −4.58% | −3.37% | −4.41% | −4.33% |
Regional average net return (weighted by total cropland acres in each county) per acre discounted over a 60-year planning horizon at a discount rate of 3% per year.
Dallam County, irrigated acreage increases 49.4% over the baseline reaching 41.1% of total acres, Hartley 22.7% to reach 44.9%, Moore 69.7% to reach 33.4%, and Sherman 70.6% to reach 43.5% (
In the irrigation technology adoption scenario, the regional average saturated thickness drops 57.5% to 64.9 feet in year 60 of the simulation, which is 4.9% higher than the baseline scenario level (
Irrigated acres as a percent of all cropland acres in this scenario increases above the baseline in year 60 by 8.57% to 29.59% of all acres (
In the water use restriction scenario, the regional average saturated thickness drops 49.7% to 76.9 feet in year 60 of the simulation, which is 24.4% higher than the baseline scenario level (
Irrigated acres as a percent of all cropland acres in this scenario increase above the baseline in year 60 by 8.67% to reach 29.62% of all acres (
The regional average saturated thickness drops 57.54% to 64.9 feet in year 60 in the temporary conversion to dryland scenario, which is 4.90% higher than the baseline scenario level (
Average irrigated acres as a percent of all cropland acres in this scenario increase above the baseline in year 60 by 10.15% to reach 30.02% of all acres (
The permanent conversion to dry land scenario provided results similar to the temporary conversion to dry land scenario. Under the permanent conversion policy, the regional average saturated thickness drops 57.53% to 64.9 feet in year 60 of the scenario, which is 4.94% higher than the baseline scenario level (
Average irrigated acres as a percent of all cropland acres in this scenario increased above the baseline in year 60 by 10.24% to reach 30.04% of all acres (
Following are the major conclusions drawn from this research:
The policies that show the most favorable results in terms of conserving the water available in the Ogallala Aquifer are the biotechnology adoption scenario and the water use restriction scenarios. Both policies assume a 1% reduction in water use per year during the 60-year planning horizon.
The permanent conversion to dry land scenario proves to be the third best in water conservation, though it is just marginally better than the temporary conversion to dry land and the irrigation adoption scenarios.
The effect of each policy on the saturated thickness in the individual counties varies primarily due to the dependence each county has on irrigated acreage. For example, Sherman County has the greatest water savings in terms of ending saturated thickness in both the biotechnology and water use restriction scenarios when compared with the baseline scenario, but it also has the second least irrigated acreage as a percent of total cropland acres.
There are also differences among the counties in terms of the specific crops planted in each contributing to differences in the scenario results. Dallam and Hartley have a high percentage of their cropland planted in irrigated corn and irrigated wheat with Dallam having 46.6% in irrigated corn and 28.4% in irrigated wheat and Hartley having 49.8% in irrigated corn and 25.4% in irrigated wheat.
Moore and Sherman counties, however, have a greater reliance on dry land crops. In Moore County, 34.5% of all cropland acreage is in dry land wheat, 23.6% in irrigated corn, and 14.5% in irrigated wheat. In Sherman County, dry land wheat accounts for 32.3% of all cropland acres, while irrigated corn accounts for 25.3% and irrigated wheat 24.3%.
In terms of economic costs, the biotechnology adoption policy by far provides the greatest net returns and net present values. The yield increases provided in the models are based on seed varieties that are currently available to producers and do not include expected improvements in the future.
The next best policy for the region and each individual county in terms of net present value of returns is the irrigation adoption technology, though it ranks last (along with the temporary conversion to dry land policy) in terms of reducing aquifer depletion. The water use restriction policy, though as effective as the biotechnology adoption policy, has the lowest net present value of returns showing that, at present, it would be the best conservation policy but at a significant cost to producers.
As is the case with most studies, there are limitations to the study at hand. These are mainly with regard to the economic model using county average hydrologic data where in reality the hydrological features may vary from one part of the county to another. Further, production functions for each county were estimated using data from the crop simulation software CropMan which is based on only one weather station and the most predominant soil type. Actual county average crop yields are used for dry land crops; however, yields can vary greatly from one area of a county to another. Also, economic parameters and irrigation technology are assumed to be constant during the planning time frame. Finally, competition among farmers and between farmers and other residents, for use of available groundwater is not included in the model.
Concerning the accelerated depletion of the Ogallala Aquifer, policy makers are faced with a daunting task of determining which policy will be the most effective at conserving the water currently available while simultaneously considering cost of implementing any of these scenarios, the economic costs of the policy in terms of lost producer returns, the resulting economic impacts on resource suppliers and on the community over all. In deciding on a policy focused on conserving water, policy makers also must consider the impact each policy will have on other segments of the industry as well as on the communities that rely on agricultural industries in the area. There will always be tradeoffs between the policy objectives and the consequences associated with that policy. This study is aimed at providing additional information to policy makers concerning the effectiveness of each of the five policies to conserve the Ogallala Aquifer in the region and the individual counties while also providing an insight into the impact each policy will have on net farm returns during the 60-year planning horizon.
The research is funded, in part, by the Ogallala Aquifer Program, a consortium of the USDA Agricultural Research Service, Kansas State University, Texas AgriLife Research, Texas AgriLife Extension Service, Texas Tech University, and West Texas A & M University. This research is also supported, in part, by the Killgore Research Center and Dryland Agriculture Institute of West Texas A & M University.
Almas, L.K., Guerrero, B.L., Lust, D.G., Fatima, H., Tewari, R. and Taylor, R. (2017) Extending the Economic Life of the Ogallala Aquifer with Wa- ter Conservation Policies in the Texas Panhandle. Journal of Water Resource and Protection, 9, 255-270. https://doi.org/10.4236/jwarp.2017.93017