Integrating Climate Variability and Forecasts into Risk-Based Management Tools for Agricultural Production and

Resource Conservation

Jean L. SteinerJurgen D. GarbrechtJeanne M. SchneiderX. C. (John) Zhang

M. W. Van Liew

USDA-ARS Grazinglands Research Laboratory

Great Plains Agroclimate and Natural Resources Unit

El Reno, OK

Objectives

• Regional context of Southern Great Plains

• research focus• Methods • Assessing decision maker needs• Relevance to GECAFS

El Reno, OK

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

Methods and Preliminary Analyses

0

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160 M

ean

Pre

cipi

tatio

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Mea

n Te

mpe

ratu

re, C

J F M A M J J A S O N DMonth

Precipitation Temperature

El Reno, Oklahoma – 1971 to 2000

Year

Prec

ipit

atio

n [i

n]

Dry PeriodsWet Periods

Annual Precipitation

5-yr weighted average CD3405; 1895-2003152025303540455055

1895 1915 1935 1955 1975 1995

Annual Precipitation in Central Oklahoma

USDA-ARS-GRL

Calendar Year

Annu

al S

trea

mflo

w [

cfs]

Annual Precipitation [in]Blue River, Oklahoma

Blue River Streamflow and PrecipitationPrecipitationStreamflow

5-yr weighted average

Average for1937-2003

R2 = 0.84USGS 07332500

USDA-ARS-GRL

Blue River StreamflowPr

obab

ility

of E

xcee

danc

e

1981-2002

1947-1980

Streamflow [cfs]USDA-ARS-GRL

USDA-ARS-GRL

USDA-ARS-GRL

USDA-ARS-GRL

CPC precipitation forecasts product

USDA-ARS-GRL

Dependability of Wet Forecasts, |DN| ≥ 10% Lead Time 0.5 months, 58 forecasts from JFM 1997 through OND 2001

< 50% 50-99% 100%

1/12/2

2/2

1/12/2

1/12/2

2/22/23/31/1

2/23/3

4/44/5

2/2

4/44/4

4/53/4

4/53/3

4/4

4/4

3/33/3

4/4

3/4 1/1

2/2

2/23/3

2/3

2/2

4/55/7

4/65/66/7

6/6

5/75/7

4/6

4/6

5/7

5/76/7

5/7

4/7

1/21/2

1/2

1/2

2/4

4/8

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

3/31/1

1/1

2/2

6/8

5/8 9/13

6/6

10/12

10/11

2/22/2

1/1

1/12/2

2/3

2/2

1/11/1

3/4

7/8

10/14

12/18

10/14

17/19

9/14

12/16

1/2

1/2 2/3

1/1

1/2

1/1

1/21/2

3/6

2/3

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

location normal

location forecast =

location normal ++ forecast

anomalies

divisionforecast

locationforecast

divisionforecast

locationnormal

divisionnormal

Very WetVery Dry PRECIPITATION

PROB

ABIL

ITY

OF E

XCEE

DANC

E

forecast anomalies = division forecast - division normal

Spatial Downscaling of Forecasts

SEP

NO

V

JAN

MA

R

MAY JU

L

SEP

NO

V

JAN

Full cycle of 13 3-month forecasts

Desired set of 151-month forecasts

Simulated grain yield, kg/m2

0.1 0.2 0.3 0.4

Cum

ulat

ive

prob

abili

ty

0.0

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Dry-40%Dry-70%Avg-40%Avg-70%Wet-40%Wet-70%

Month

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

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thly

mea

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ecip

itatio

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200Wet-NWSWet-CLIGENAvg-NWSAvg-CLIGENDry-NWSDry-CLIGEN

Evaluating a climate generator (CLIGEN) for daily precipitation…

… and wheat growth model sensitivity to precipitation terciles and initial soil water condition

50%

0%

100%

forecast

Very Wet

Very DryPRECIPITATION

PRO

BABI

LITY

OF

EXCE

EDAN

CE

normal

100%

Currentlyunknown…

forecastnormal

PRO

BABI

LITY

OF

EXCE

EDAN

CE

0%

50%

100%

Very Low Very High3-MONTH PRECIPITATION

50%

0%

forecast

yield

Very High

Very LowFORAGE YIELD

PRO

BABI

LITY

OF

EXCE

EDAN

CE

normalyield

100%

What is the relationshipbetween a sequence offorecasts and outcome?

50%

0%

forecast

yield

Very HighVery LowFORAGE YIELD

PRO

BABI

LITY

OF

EXCE

EDAN

CE

normalyield

100%Associate baseline and forecast odds for outcomes with economic factors to define “risks”.

Models Used

• Regional, watershed– SWAT– Neural Networks

• Farm/field Level– WEPP– CERES– Enterprise budgets, market tools

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”

Decision Making Case Study

Cropping/Grazing

Systems in Southern Great Plains

Month Agricultural Management Calendars for S. Great Plains Decision Points

Wheat SummerPerennial

WinterPerennial

SummerAnnual

StockerCattle

January graze

February graze grow to grain?spring fertilizer?

March bale wheat in May?

April

May

June harvest summer crop?

July contract for cattle?#, delivery date, $

August sell

September

October sow area to plant, which fields first,variety, seeding rate, fertilizeramount

November delivery

December graze

Decision Points: Wheat Grazing Systems

foragequality dip

graze

sow

graze

buy additional cattle?

sell cattle?

supplemental feed?

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

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

Business Decisions

• Marketing/hedging• Diversification of farm

enterprises• Off-farm income

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

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

Regional Case Study

Water Release

from Reservoirs

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

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/

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/

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

USDA-ARS-GRL

Recognizing andAdapting to Change