Weiwei Wang, Seong C. Park, Bruce McCarl , and Steve Amosson

1. Economic and Groundwater Use Implications of Climate Change and Bioenergy Feedstocks Production in the Texas Ogallala Aquifer Region Weiwei Wang, Seong C. Park, Bruce McCarl, and Steve Amosson Texas A&M AgriLife Research and Extension

2. Outline • Introduction • Motivation • Objectives • Methodology • Model Structure • Data • Results and Conclusion • Future Work

3. Ogallala Aquifer • 174,000 mi2, 8 states, largest aquifer in the world • Drinking water to 82% of the people • 30% of all groundwater for irrigation in USA • Depletion of groundwater • Aquifer capacity by 33% due to over- pumping for past 50 years. • Low recharge rate 1-2 inches/per year • Withdraws 3 X faster than recharge • Climate Change • Bioenergy feedstock production Source: U.S. Geographic Survey, 2000

4. Motivations • Conflict between limited natural resources and increasing demand of food and biofuel productions is the major concern • Accurate modeling of integrated land-water system isessential for land and water management and rural communities in the Texas High Plains Region • There is little comprehensive research on the impacts of climate change and bioenergy feedstock production on the crop mix and rural economy in Texas High Plains Region

5. Objectives • Identify optimal land and water allocation over 2011-2050 horizons. • Estimate to what extent climate change and bioenergy production may alter the short- and long-term outlook for regional food, agriculture and resource availability. • Estimate impacts of water availability on crop mix and land use change

6. Study Area 20 Years Saturated Thickness Changes in Three Counties Three Counties in THP : Dallam, Hartley and Sherman

7. Climate Change Physical Model Conceptual Model

8. Methodology Dynamic Land and Hydrologic Optimization Model • Dynamic • 40-year planning horizons (2011-2050); annual time scale • Multi-sector • Agricultural sector: • conventional crops (corn, cotton, hard red winter wheat, sorghum) and livestock; Irrigated and dry cropland , grassland/pasture; deficit irrigation; • Hydrologic sector: • saturated thickness; water pumping capacity; pumping costs; • Climate change sector: • four GCMs projections • Biofuel feedstock sector: • crop residues (corn stover and wheat straw); dedicated energy crops: switchgrass and energy sorghum • Spatial explicit • Sub county-level analysis • Homogenous hydrological regions

9. Objective Function Model Structure • Agricultural Sector

10. Model Structure • Hydrological Sector Initial ST for each zone: 25, 75 ,125, 175 and 225 ft Initial lift for each zone 400, 350, 300, 250 and 200ft • Pumping cost

11. Model Structure • Biofuel Feedstock Sector Switchgrass Sorghum

12. Data

13. Crop Yields and Water Demand Sensitivity to Climate Change Crop yields sensitivity to climate change under four GCMs Modeled, 2046-2065 relative to 2000 climate baseline. Irrigation water use sensitivity to climate change under four GCMs Modeled, 2046-2065 relative to 2000 climate baseline. (Unit: mm)

14. Study Area 20 Years Saturated Thickness Changes in Three Counties Three Counties in THP : Dallam, Hartley and Sherman

15. Study Area The proportion of identified hydrologically homogeneous zones in each county

16. Results Changes of irrigated land within different zones in three counties

17. Results Changes of cropland use in Zone 2 in three counties during 2010~2050

18. Results Changes of cropland use in Zone 4 in three Counties during 2010~2050

19. Results Baseline scenario • In the low ST zones, irrigated corn is initially replaced by irrigated wheat and sorghum then land transitions to dryland wheat, sorghum and cotton. • An increasing trend in total pasture land reflecting a shift out of cropping • Saved water from deficit irrigation will be diverted to sustain the relative low water-use crops production • With continuing water depletion, irrigated cropping will be replaced by dryland farming particularly after year 2030

20. Climate change and adaptation effects Percentage change of net present value for agricultural production over 40-year periods under four GCMs relative to the reference case (Unit: %) Percentage change of net present value over 40-year periods due to optimal crop mix adaptation strategies. (Unit: %)

21. Results: Climate Change Crop pattern changes under four GCMs Modeled in selective years relative to 2010 climate baseline in Zone 1, Dallam County

22. Results: Climate Change Crop pattern changes under four GCMs Modeled in selective years relative to 2010 climate baseline in Zone 5, Dallam County

23. Biofuel Feedstock Impacts Land use change with Switchgrass and crop residues

24. Biofuel Feedstock Impacts Land use change with energy sorghum, Switchgrass and crop residues

25. Biofuel Feedstock Impacts Crop Production Under Alternative Bioenergy Scenarios, 2022

26. Conclusions • Optimal land and water allocation varies with crop pattern, region and time • Water availability significantly affects crop choices • Most climate change projections: positive effects on the net present value of agricultural production over 40 years. • Adopting of a water conserving crop mix is an effective strategy for farmers in adapting to climate change and continuing water depletion • Drylandswitchgrass is a favorable feedstock in water-scarce regions which keeps more land in cropping • A significant land use trade-off between switchgrass and energy sorghum • Spatial explicit modeling of agricultural land use is critical for effective land and water management

27. Future Work • livestock options, conservation reserve program options, and environmental implications • Integrating more sectors and markets • Nesting with other partial or general equilibrium model (e.g. FASOM) • Taking results as input to socio-economic assessment model (e.g. IMPLAN)

28. Current Research • Soil nutrient management (manure management) • ANOVA w/ SAS, response functions w/SAS (non-nested test) • Water resource management • LP modeling with GAMS, • Air Quality: Emission abatement in cattle feedlots and dairies • Budgeting and Stochastic Dynamic Programming (SDP) w/Matlab • Sustainable regional bioenergy production model • Budgeting and simulation w/ Simetar • Technology adoptions and Risk management • ANOVA w/ SAS, Simulation

29. Thank You