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. Year Precipitation [in] Dry Periods Wet Periods Annual Precipitation 5-yr weighted average CD3405; 1895-2003 15 20 25 30 35 40 45 50 55 189519151935195519751995 Annual Precipitation in Central Oklahoma USDA-ARS-GRL

13. Calendar Year Annual Streamflow [cfs] Annual Precipitation [in] Blue River, Oklahoma Blue River Streamflow and Precipitation Precipitation Streamflow 5-yr weighted average Average for 1937-2003 R 2 = 0.84 USGS 07332500 USDA-ARS-GRL

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

18. CPC precipitation forecasts product USDA-ARS-GRL

19. Dependability of Wet Forecasts, |D N | ≥ 10% Lead Time 0.5 months, 58 forecasts from JFM 1997 through OND 2001 < 50% 50-99% 100% 1/1 2/2 1/1 2/2 1/1 2/2 3/31/1 2/23/3 4/4 4/5 2/2 4/4 4/5 3/4 4/5 3/3 4/4 3/3 4/4 3/4 1/1 2/2 3/3 2/3 2/2 4/5 5/7 4/6 5/6 6/7 6/6 5/7 4/6 5/7 6/7 5/7 4/7 1/2 2/4 4/8

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

21. First: Downscale Forecasts to Farm 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. location normal location forecast = + location normal + forecast anomalies division forecast location forecast division forecast location normal division normal Very Wet Very Dry PRECIPITATION PROBABILITY OF EXCEEDANCE forecast anomalies = division forecast - division 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. 50% 0% 100% forecast Very Wet Very Dry PRECIPITATION PROBABILITY OF EXCEEDANCE normal 100% Currently unknown… forecast normal PROBABILITY OF EXCEEDANCE 0% 50% 100% Very LowVery High 3-MONTH PRECIPITATION 50% 0% forecast yield Very High Very Low FORAGE YIELD PROBABILITY OF EXCEEDANCE normal yield 100% What is the relationship between a sequence of forecasts and outcome?

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

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 forage quality dip graze sow graze buy additional cattle? sell cattle? 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