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 Slide 2 Objectives Regional context of Southern
Great Plains research focus Methods Assessing decision maker needs
Relevance to GECAFS Slide 3 El Reno, OK Slide 4 Slide 5 Slide 6
Slide 7 Slide 8 Slide 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 Slide 10 Methods and Preliminary Analyses Slide 11 El
Reno, Oklahoma 1971 to 2000 Slide 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 Slide 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 Slide
14 Blue River Streamflow Probability of Exceedance 1981-2002
1947-1980 Streamflow [cfs] USDA-ARS-GRL Slide 15 Slide 16 Slide 17
Slide 18 CPC precipitation forecasts product USDA-ARS-GRL Slide 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 Slide 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 Slide 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
Slide 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 Slide 23 Slide 24 Evaluating a climate generator (CLIGEN)
for daily precipitation and wheat growth model sensitivity to
precipitation terciles and initial soil water condition Slide 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? Slide 26
Slide 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.
Slide 28 Models Used Regional, watershed SWAT Neural Networks
Farm/field Level WEPP CERES Enterprise budgets, market tools Slide
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 Slide 30 Decision Making
Case Study Cropping/Grazing Systems in Southern Great Plains Slide
31 Slide 32 Decision Points: Wheat Grazing Systems forage quality
dip graze sow graze buy additional cattle? sell cattle?
supplemental feed? Slide 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 Slide 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 Slide 35 Business Decisions Marketing/hedging
Diversification of farm enterprises Off-farm income Slide 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 Slide 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 Slide 38 Regional Case
Study Water Release from Reservoirs Slide 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 Slide 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/ Slide 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/ Slide 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 Slide 43
USDA-ARS-GRL Recognizing and Adapting to Change
