1. An Analysis of the Effectiveness of Cost- Share Irrigation Programs in the High Plains Aquifer ~Josh Roe

2. Statement of the Problem: Agriculture is the most prominent industry in W. KS. Agriculture is the most prominent industry in W. KS. Since W. KS receives very little precipitation annually, irrigation is a key component of the Agricultural Industry. Since W. KS receives very little precipitation annually, irrigation is a key component of the Agricultural Industry. An overwhelming majority of this water is supplied by the Ogallala Aquifer. An overwhelming majority of this water is supplied by the Ogallala Aquifer.

3. Statement of the Problem: Facts about the Ogallala and W. KS: The Ogallala Aquifer covers seven states and has turned 16 million acres of dryland ground into highly productive land since 1930. The Ogallala Aquifer covers seven states and has turned 16 million acres of dryland ground into highly productive land since 1930. Irrigation accounts for 87% of all water used in W. KS. Irrigation accounts for 87% of all water used in W. KS. Irrigation provides an estimated value of $795 million of income to the W. KS economy annually. Irrigation provides an estimated value of $795 million of income to the W. KS economy annually. Since 1980, areas of W. KS have seen experienced declines in the saturated thickness of the Ogallala as great as 2.6 feet per year. Since 1980, areas of W. KS have seen experienced declines in the saturated thickness of the Ogallala as great as 2.6 feet per year. Numerous resources have been devoted towards studies on groundwater sustainability in W. KS. since the early 1970’s Numerous resources have been devoted towards studies on groundwater sustainability in W. KS. since the early 1970’s

4. Data: For every water right held, Irrigators in W. KS, since 1988, have been required to report: For every water right held, Irrigators in W. KS, since 1988, have been required to report: -Annual Water Use -Crops Irrigated -Type of Irrigation System -Depth to Water Numerous possibilities for reporting error. Numerous possibilities for reporting error. Over 1,500 man hours spent on transforming the data into a reliable source. Over 1,500 man hours spent on transforming the data into a reliable source. Finished Product: Around 500,000 observations and 100 unique variables. Finished Product: Around 500,000 observations and 100 unique variables.

5. Do Water-Saving Irrigation Systems Save Water? An obvious solution to decreasing water pumped… adopting more efficient systems?? An obvious solution to decreasing water pumped… adopting more efficient systems?? Acre-Feet Pumped: G=C+L Acre-Feet Pumped: G=C+L - G=Gross Acre-Feet Pumped -C=Acre-Feet Consumed by the Crop -L=Water Loss Efficiency=C/G Efficiency=C/G Does an Increase in E imply a decrease in G? Does an Increase in E imply a decrease in G?

6. Technology Adoption: Irrigators have adopted more efficient technologies based on economic feasibility. Irrigators have adopted more efficient technologies based on economic feasibility. Cost sharing programs for more efficient technologies were added to the EQIP in the 2002 Farm Act. Cost sharing programs for more efficient technologies were added to the EQIP in the 2002 Farm Act. - Range from 30-75%, depending on the region/current technology

7. Levels of Efficiency: Flood Irrigation: Flood Irrigation: -Least Expensive/Efficient -E≈0.50 Pivot Irrigation: Pivot Irrigation: -More Expensive/Efficient - E≈0.90

8. Levels of Efficiency: Continued Sub Surface drip irrigation (SDI): Sub Surface drip irrigation (SDI): -Most Expensive/Efficient - E≈0.99

9. Effects of Increased Efficiency: Case 1: 12 in/ac applied to crop (E=0.50): G=C+L → 12=6+6 Case 1: 12 in/ac applied to crop (E=0.50): G=C+L → 12=6+6 Six inches of water will NOT optimize corn yields! Six inches of water will NOT optimize corn yields! Case 2: 12 in/ac applied to crop (E=0.90): G=C+L → 12=10.8+1.2 Case 2: 12 in/ac applied to crop (E=0.90): G=C+L → 12=10.8+1.2 Increased E: Increased E: -Should Increase Yield -May Increase or Decrease Gross Water Pumped

10. Project 1: Modeling the Demand for Water for Corn Irrigation The overall objective was to quantify the factors that an individual producer weighs when deciding how much water to pump. The overall objective was to quantify the factors that an individual producer weighs when deciding how much water to pump. The model was also be used to forecast water use for corn irrigation under different output and energy price scenarios. The model was also be used to forecast water use for corn irrigation under different output and energy price scenarios.

13. Index year = (Π electricity *P electricity ) + (Π gasoline *P gasoline ) + (Π propane *P propane ) + (Π diesel *P diesel )

15. Modeling: The dependant variable was acre-feet pumped. The dependant variable was acre-feet pumped. The 17 independent variables include: The 17 independent variables include: –Yearly Well-Yield and Number of Acres Irrigated –Yearly Hydrologic Data –Yearly Corn, Energy, and Input prices –Annual Precipitation by Irrigation District, in 3 categories –Dummy Variables for Location in W. KS –Dummy Variables for Type of Irrigation System in a Given Year

16. Results: R2R2 0.678 Standard Error62.44 VariableParameterT-StatElasticity Intercept347.477.73n/a Year2.192.550.072 Pump Rate0.1346.830.526 Acres Irr0.7054.240.518 Lag Price54.886.810.352 EIndex-70.81-5.94-1.005 Depth to Water0.1213.580.098 Sat Thick0.1110.560.082 Rain 1-10.09-14.46-0.126 Rain 21.335.440.059 Rain 3-2.61-22.20-0.172 Price Index-2.18-5.99-1.531 District 1-22.70-16.16-0.136 District 2-21.30-9.50-0.127 Hpivot-11.07-7.49-0.069 LPivot-4.62-2.52-0.029 Other-5.73-1.72-0.036 Sprinkler-4.60-0.60-0.029

18. Conclusions: The model’s prediction accuracy is reduced in years where rainfall amounts are far above or below average. The model’s prediction accuracy is reduced in years where rainfall amounts are far above or below average. Overall, the model satisfactorily captured the economic factors that a corn producer weighs in deciding how much water to pump. Overall, the model satisfactorily captured the economic factors that a corn producer weighs in deciding how much water to pump. The nature of the model makes it very diverse. The nature of the model makes it very diverse.

19. Project 2: Groundwater Use Trends and Forecasts in Western Kansas A multinomial logit model was estimated to predict a producer’s planting decision. A multinomial logit model was estimated to predict a producer’s planting decision. Based on this decision, the amount of water pumped was estimated. Based on this decision, the amount of water pumped was estimated. Counter-factual policy/price scenarios conducted. Counter-factual policy/price scenarios conducted.

20. Logit Model: P(plant crop x) = f (hydrology, technology, prices, weather) Variable Marginal Effect EPALF-0.0061 EPCORN0.32 EPMILO-0.10 EPSOY0.038 EPWHEAT-0.20 EINDEX-0.35 PRICE INDEX 0.017 FLOOD0.12 HPIVOT0.16 LPIVOT0.16 LAG CORN 0.57 FAIR-0.19

21. Simulation 3: No technology adoption

22. Conclusions Several shifts in water use trends during 1990s Several shifts in water use trends during 1990s Low commodity prices held water use down Low commodity prices held water use down Higher energy prices would have reduced water use further Higher energy prices would have reduced water use further New technologies slightly reduced water use New technologies slightly reduced water use

23. Peterson and Ding, “Do Water-Saving Irrigation Systems Save Water?” Empirically estimated the effects of increased efficiency on water use. Empirically estimated the effects of increased efficiency on water use. Constructed a Just-Pope production function from optimal irrigation scheduling models and a daily-loop plant growth estimator. Constructed a Just-Pope production function from optimal irrigation scheduling models and a daily-loop plant growth estimator. Estimated production and water use data were combined with actual technology choices and hydrologic data to determine the producer’s technology choice and water use. Estimated production and water use data were combined with actual technology choices and hydrologic data to determine the producer’s technology choice and water use.

24. Results: ItemFloodPivotDrip Water Volume Pumped (acre feet)314.5252.0305.47 Yield (bu/ac)147.15167.58183.37 Pumping Costs ($/ac)45.0662.6455.90 Return to Land and Capital ($/ac)118.32151.09190.05 Standard Deviation ($/ac)98.73117.46140.65 Net Return ($/ac)77.2577.2671.12 Net Return (30% Cost Share)77.2577.26106.80

25. Done Deal??? According to the results thus far: increased efficiency=less water pumped! According to the results thus far: increased efficiency=less water pumped! However, I am not satisfied! However, I am not satisfied! Why? WATER SPREADING!! Why? WATER SPREADING!!

26. Water Spreading?? All previous studies mentioned assumed that acres irrigated did not change with increased efficiency! All previous studies mentioned assumed that acres irrigated did not change with increased efficiency! Mainly, in conversion from flood to pivot irrigation, they assumed that acres irrigated decreased from 160 to 126!! Mainly, in conversion from flood to pivot irrigation, they assumed that acres irrigated decreased from 160 to 126!! What if, in many cases, acres irrigated increased?? What if, in many cases, acres irrigated increased??

27. Initially: producer faces: E=0.5 and pumps 155.02 acre feet per-well with a constant C: Initially: producer faces: E=0.5 and pumps 155.02 acre feet per-well with a constant C: Now: producer faces E=0.9 and still pumps 155.02 acre feet per-well with a constant C: Now: producer faces E=0.9 and still pumps 155.02 acre feet per-well with a constant C:

28. Extreme Case?? The Previous Example was too Extreme! The Previous Example was too Extreme! However, what if producers optimized corn yield…Still room for more acres?? However, what if producers optimized corn yield…Still room for more acres?? Now producer faces: E=0.5 and 155.02 acre-feet pumped, but increases C to 1: Now producer faces: E=0.5 and 155.02 acre-feet pumped, but increases C to 1:

29. Spreading Under Optimal Irrigation: Producer Upgrades to Pivot: E=0.9 and 155.02 acre-feet pumped, but still chooses optimal irrigation: Producer Upgrades to Pivot: E=0.9 and 155.02 acre-feet pumped, but still chooses optimal irrigation: Acres irrigated increases by 80% in both cases! Acres irrigated increases by 80% in both cases! Has this transpired in reality? Has this transpired in reality?

30. Change in Acres From Switching From Flood To Pivot Acres Year Number of AdoptersTotal Acreage ChangeMean 199227-249.00-9.22 199324-11.00-0.45 199419326.0017.15 199521146.006.95 199625110.004.4 199726286.0011 199816-253.00-15.81 1999963.007 20007243.0034.71 Total174661.00

31. Conclusions Water Spreading has not occurred “Optimally”. Water Spreading has not occurred “Optimally”. Land is a fixed input in many cases. Land is a fixed input in many cases. However, water spreading has attributed towards 20% of the increases in irrigated acreage. However, water spreading has attributed towards 20% of the increases in irrigated acreage. Nonetheless, water spreading is a viable phenomenon. Nonetheless, water spreading is a viable phenomenon. Cost-sharing programs lead to increases in yield, but have small effects on sustaining water. Cost-sharing programs lead to increases in yield, but have small effects on sustaining water.