Impact of Weather Derivatives
on Water Use and Risk
Management in Georgia
Shanshan Lin (presenting), Jeffrey D. Mullen and Gerrit
Hoogenboom
Agricultural and Applied Economics
The University of Georgia
May. 2007
Funded by USDA Special Grant #PA 2005-06007
13-Oct-04
Flint River Basin TAC
Background
 Water
scarcity is an emerging issue in
Georgia
 Agriculture
is primary consumptive water
user
 Need
to increase water application
efficiency
 Technology-based
limitations
approaches have
Objectives of this Presentation

Demonstrate irrigation is a viable risk
management strategy

Examine effect of water pricing policy on optimal
irrigation strategies

Investigate the impact of financial instruments on
production risk and optimal water use
Methodology




Economic Model (Maximize Expected Utility).
Irrigation application criteria (plant-available water
threshold)
Crop simulation model (DSSAT)
Design of the proposed weather derivative product
(choice variables: i*, λ, x)
Data

4 Crops : Corn, Cotton, Peanut, Tomato

3 Soils : Wagram Sand, Tifton Loam Sand, and Norfolk
Loam Sand

3 Locations : Mitchell, Miller, and Lee Counties

Weather Data : Daily Solar Radiation, Temp. (Max & Min),
and Precipitation

Irrigation Cost per Application : Fixed and Variable
Results

(1) Impact of the optimal irrigation on
producers’ Certainty Equivalent Revenue
CER
Impact of Irrigation on CER
3500
3000
2500
2000
1500
1000
500
0
1 l2 l3
l
i
i
i
so so so
r=6
Optimal
Irrigation
Without
Irrigation
1 l2 l3
l
i
i
i
so so so
r=1.1
 (2)
Impact of Potential Water Pricing Policy
on producers’ Irrigation Decision and
Certainty Equivalent Revenue
Without Weather Derivative
Contract
Mitchell_Corn
IrrTarget Irr Amount CER
Irri Cost=30.8
r=6
soil1
50_soil1
310.16 1920.12219
soil2
70_soil2
1851.874
217.72
soil3
25_soil3
169.72 2469.70957
r=1.1
soil1
soil2
soil3
60_soil1
65_soil2
25_soil3
339.24 2448.20421
203.52 2389.63599
169.72 3006.86069
IrrTarget Irr Amount CER
Irri Cost=80
50_soil1
65_soil2
20_soil3
310.16 1750.19578
203.52 1724.97357
157.16 2372.2936
60_soil1
65_soil2
25_soil3
339.24 2303.45927
203.52 2301.67624
169.72 2934.12952
With Weather Derivative Contract
Mitchell_Corn
IrrTarget Irr Amount
Irri Cost=30.8
r=6
soil1
50_soil1
310.16
soil2
70_soil2
217.72
soil3
25_soil3
169.72
r=1.1
soil1
60_soil1
339.24
soil2
65_soil2
203.52
soil3
25_soil3
169.72
CER
IrrTarget Irr Amount CER
Irri Cost=80
1965.06738
1929.85938
2517.06635
45_soil1
70_soil2
20_soil3
300.16 1812.49435
217.72 1813.56031
157.16 2433.2775
2453.93524
2397.52688
3011.63829
60_soil1
65_soil2
25_soil3
339.24 2311.15094
203.52 2311.29904
169.72 2940.2716
•
Cumulative water use for
corn, cotton, peanut, and
tomato in Mitchell, Miller,
and Lee
Impact of WD on Water Use
Water Use (acre feet)
(3)Impact of Weather
Derivative on Water
Use
625000
620000
615000
610000
605000
600000
595000
590000
585000
580000
Without Weather
Derivative
Contract
With Weather
Derivative
Contract
r=6
r=1.1
Impact of Weather Derivative on
Farmer Welfare

Regardless of risk
aversion, better off

even though the
premium included a
10% proportional load.
Impact of WD on CER_Mitchell Corn
3500
3000
2500
2000
Without Weather
Derivative
1500
With Weather
Derivative
1000
500

One exception in Lee
County
• the decreases in CER
are very small.
0
soil1 soil2 soil3
r=6
soil1 soil2 soil3
r=1.1
Conclusion
 Irrigation
is as an important risk
management strategy in agricultural
production.
 The
proposed water pricing policy may
have limited effect on irrigation water use.

Even when precipitation derivative is offered
Conclusions (Cont.)
 A precipitation
insurance contract could be
an attractive risk management tool for a
variety of crop producers in Georgia
 May
reduce water use while increasing
farmer welfare.
 Thank
you
 Questions?
Economic Model
Decision Criteria in the Presence of Risk


Maximize Expected Utility
Utility curve

Risk Averse: concave utility
u   0; u  0


Risk Neutral: linear utility
Risk aversion : the degree of
concavity of the utility function
Ra ( y )  

u 
u
Decreasing absolute risk
aversion-
u  0
R1
U 
1 
Presentation Outline



Objective : develop a dynamic model that conceptualizes
irrigation and financial decisions of farmers who face
weather uncertainty and vary in their risk preferences.
Methodology
- Expected Utility Model
- Crop Growth Simulation Model
- Weather Derivative Design
Results and Discussions
Why irrigation in a humid area
 Economic
benefit to region (Makes land
much more productive )
 Allows
 Offset
year-round production
the impact of rainfall variability on
crop yield and to reduce the risk
associated with weather variability.
Irrigation Growth timeline
1,600,000
Irrigated Area (acres)
1,400,000
Sod & Nursery Farms
1,200,000
Tobacco & Other
1,000,000
Pastures & Small Grain
Orchards
800,000
Vegetables
600,000
Corn
400,000
Peanuts
Cotton
200,000
0
1970
1975
1977
1980
1982
1986
1989
1992
1995
1998
2000
Precipitation data
Weather Derivative Design
Weather data
Soil data
Crop Property data
Crop Management data
Plant simulation
model
f
Payoff: f
Premium: π
NRwithout
weather
derivative
Economic Model
 ( NRwithout )t  ft (it | x, i* ,  )   ( x, i* ,  ) 
Max( EU )  
1 
t 1976
1
2000
 h(it )
1
(qPcrop  wC pumping )t  f t (it | x, i* ,  )   ( x, i* ,  ) 
 
1
t 1976
2000
 h(it )
CER
 U(CER)=EU(R)
 U(R)=(R^(1-r))/(1-r)
 U(CER)=(CER^(1-r))/(1-r)