Jean L. Steiner Jurgen D. Garbrecht Jeanne M. Schneider X. C. (John) Zhang M. W. Van Liew

1. Integrating Climate Variability and Forecasts into Risk-Based Management Tools for Agricultural Production and Resource Conservation Jean L. Steiner Jurgen D. Garbrecht Jeanne M. Schneider X. C. (John) Zhang M. W. Van Liew USDA-ARS Grazinglands Research Laboratory Great Plains Agroclimate and Natural Resources Unit El Reno, OK

2. Objectives • Regional context of Southern Great Plains • research focus • Methods • Assessing decision maker needs • Relevance to GECAFS

3. El Reno, OK

9. Research Focus • Risk-based decision making • Climate variability as a primary risk factor • Decadal scale cycles • Seasonal forecasts • Levels of analysis • Regional, watershed • Farm-scale

10. Methods and Preliminary Analyses

11. El Reno, Oklahoma – 1971 to 2000

12. 55 Annual Precipitation Dry Periods Wet Periods 50 45 40 Precipitation [in] 35 30 25 20 5-yr weighted average CD3405; 1895-2003 15 1895 1915 1935 1955 1975 1995 Year USDA-ARS-GRL Annual Precipitation in Central Oklahoma

13. USDA-ARS-GRL Blue River Streamflow and Precipitation Precipitation 5-yr weighted average R2 = 0.84 Streamflow USGS 07332500 Annual Precipitation [in] Annual Streamflow [cfs] Average for 1937-2003 Blue River, Oklahoma Calendar Year

14. 1981-2002 Probability of Exceedance 1947-1980 Streamflow [cfs] USDA-ARS-GRL Blue River Streamflow

15. USDA-ARS-GRL

16. USDA-ARS-GRL

17. USDA-ARS-GRL

18. USDA-ARS-GRL CPC precipitation forecasts product

19. Dependability of Wet Forecasts, |DN| ≥ 10% Lead Time 0.5 months, 58 forecasts from JFM 1997 through OND 2001 < 50% 50-99% 100% 1/2 1/1 1/2 3/4 1/2 3/3 1/2 2/2 2/2 2/2 2/4 5/7 1/1 2/2 4/5 1/1 4/8 2/2 2/2 2/2 4/6 2/3 2/2 1/1 5/6 1/1 3/3 2/2 6/7 5/7 4/7 3/3 2/2 4/6 5/7 4/5 6/7 4/4 6/6 4/6 5/7 2/2 4/4 5/7 3/3 5/7 3/3 4/4 4/5 3/3 4/5 4/4 4/4 3/4 4/4

20. Dependability of Dry Forecasts, |DN| ≥ 10% Lead Time 0.5 months, 58 forecasts from JFM 1997 through OND 2001 < 50% 50-99% 100% 5/5 1/1 2/3 1/2 3/3 1/1 1/1 2/2 2/2 2/3 1/1 1/2 2/3 1/1 1/1 1/1 10/14 1/1 10/14 1/2 7/8 9/14 2/2 3/4 12/18 1/2 1/2 6/6 17/19 2/2 9/13 12/16 5/8 2/2 3/6 10/12 6/8 10/11

21. First: Downscale Forecasts toFarm and Monthly Scales Second: Use Weather Generators to Produce Sequences of Daily Weather Third: Use Models to Produce Forecast Shifts in Odds for an Application Fourth: Incorporate Climate Information in Decision Support Tools

22. forecast anomalies= division forecast- division normal division forecast location normal PROBABILITY OF EXCEEDANCE division normal • Very Dry PRECIPITATION • Very Wet location forecast = location normal+ forecast anomalies location forecast division forecast location normal Spatial Downscaling of Forecasts

24. Evaluating a climate generator (CLIGEN) for daily precipitation… … and wheat growth model sensitivity to precipitation terciles and initial soil water condition

25. 100% 100% What is the relationship between a sequence of forecasts and outcome? forecast forecast normal normal PROBABILITY OF EXCEEDANCE PROBABILITY OF EXCEEDANCE 50% 50% 100% 100% 0% 0% Very Low Very High • Very Dry • Very Wet forecast yield 3-MONTH PRECIPITATION PRECIPITATION normal yield PROBABILITY OF EXCEEDANCE 50% Currently unknown… 0% • Very Low • Very High FORAGE YIELD

27. Associate baseline and forecast odds for outcomes with economic factors to define “risks”. 100% forecast yield normal yield 50% PROBABILITY OF EXCEEDANCE 0% • Very Low • Very High FORAGE YIELD

28. Models Used • Regional, watershed • SWAT • Neural Networks • Farm/field Level • WEPP • CERES • Enterprise budgets, market tools

29. Identifying Decision Maker Needs Workshops to present findings and engage in dialog One-on-one discussions of specific issues Exploratory work in form of “case studies”

30. Decision Making Case Study Cropping/Grazing Systems in Southern Great Plains

32. Decision Points: Wheat Grazing Systems buy additional cattle? graze sell cattle? forage quality dip sow graze supplemental feed?

33. Agronomic Decisions • Crop selection • e.g., maize/sorghum/millet • Long vs short season varieties • Planting density and geometry • Fertility levels, dates, rates… • Area to be planted

34. Crop/livestock system Decisions • Future stocking rates • Forage (grazed or hayed) vs grain harvest • Intensity and timing of grazing • Supplemental feed • Purchase, selling, or movement of animals

35. Business Decisions • Marketing/hedging • Diversification of farm enterprises • Off-farm income

36. Decision Maker Needs • Work with individual farmers, extension, conservationists • Identify their goals and priorities • Identify their resources and characterize their systems • Develop climate scenarios relevant to key decisions

37. Decision Maker Needs • Focus on record keeping is essential • A “journaling” tool will be used to analyze decision points, factors considered in taking decisions, building decision trees or decision rules

38. Regional Case Study Water Release from Reservoirs

39. Decision Maker Needs • Work with agencies with management responsibilities (e.g., U.S. Bureau of Reclamation, U. S. Corps of Engineers) • Understand stakeholders and issues • Analyze decision criteria and decision trees specific to their situation • Incorporate climate variability and climate forecast scenarios

40. Risks in Farming • Risk is an important aspect of the farming business. The uncertainties of weather, yields, prices, government policies, global markets, and other factors can cause wide swings in farm income. • Risk management involves choosing among alternatives that reduce the financial effects of such uncertainties.  http://www.ers.usda.gov/Briefing/RiskManagement/

41. Types of Risks • Production risk derives from the uncertain natural growth processes of crops and livestock. Weather, disease, pests, and other factors affect both the quantity and quality of commodities produced. • Price or market risk refers to uncertainty about the prices producers will receive for commodities or the prices they must pay for inputs. • Financial risk results when the farm business borrows money and creates an obligation to repay debt. Rising interest rates, the prospect of loans being called by lenders, and restricted credit availability are also aspects of financial risk. • Institutional risk results from uncertainties surrounding government actions. Tax laws, regulations for chemical use, rules for animal waste disposal, and the level of price or income support payments are examples of government decisions that can have a major impact on the farm business. • Human or personal risk refers to factors such as problems with human health or personal relationships that can affect the farm business. Accidents, illness, death, and divorce are examples of personal crises that can threaten a farm business. http://www.ers.usda.gov/Briefing/RiskManagement/

42. Relevance to GECAFS DSS • Decision making is individualized process and may be approached as case study • Decision makers have multiple objectives, some economic and some not, which must be balanced

43. USDA-ARS-GRL Recognizing and Adapting to Change