Alternative Irrigation Water Management Strategies to Conserve Water
Alternative Irrigation Water Management Strategies to Conserve Water
Alternative Irrigation Water Management Strategies to Conserve Water
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<strong>Alternative</strong> <strong>Irrigation</strong> <strong>Water</strong> <strong>Management</strong><br />
<strong>Strategies</strong> <strong>to</strong> <strong>Conserve</strong> <strong>Water</strong><br />
SDSU, Ag and Biosystems Engineering<br />
Derek M. Heeren, Hal D. Werner, Todd P. Trooien<br />
2007 Eastern SD <strong>Water</strong> Conference<br />
52 nd Annual Midwest Ground <strong>Water</strong> Conference<br />
Sioux Falls, SD<br />
Oc<strong>to</strong>ber 31, 2007
<strong>Irrigation</strong> <strong>Water</strong> Use<br />
65% of fresh<br />
water use in the<br />
Western 22 states<br />
<strong>Irrigation</strong><br />
Other<br />
USGS, 2000
Already a limited supply…<br />
Drought<br />
Increasing demand on water for<br />
municipalities and environmental<br />
purposes<br />
Recent limits on water use for some<br />
irrigation districts in SD and NE
<strong>Irrigation</strong> <strong>Management</strong>:<br />
A Paradigm Shift?<br />
“…irrigated agriculture will be called upon<br />
<strong>to</strong> produce up <strong>to</strong> 2/3 of the increased food<br />
supply…”<br />
Increasing pressure on water resources<br />
New goal: “maximization of net benefits”<br />
Require greater understanding of<br />
irrigation-yield relationships<br />
English et al., 2002
Deficit <strong>Irrigation</strong> at Field Scale<br />
Deficit irrigation has been demonstrated<br />
at the field scale<br />
76% irrigation with 93% yield<br />
57% irrigation with 84% yield<br />
Klocke et al., 2004
A Faster Way?<br />
Using a crop model (instead of field<br />
testing)<br />
• Less time<br />
• Less money<br />
Objective:<br />
• Demonstrate an effective method for using<br />
DSSAT <strong>to</strong> develop irrigation strategies
Two Questions:<br />
How much water can be conserved<br />
without affecting yield?<br />
If a given amount of water is available,<br />
what strategy should be used <strong>to</strong> optimize<br />
yield?
Application<br />
<strong>Strategies</strong> can be incorporated in<strong>to</strong><br />
SDSU <strong>Management</strong> Software<br />
Producer selects best option based<br />
on water availability<br />
Real time moni<strong>to</strong>ring and<br />
adjustment
Scope<br />
<br />
<br />
16 <strong>to</strong> 24 years of weather data<br />
Sites across the Great Plains:<br />
• Oakes, ND<br />
• Brookings, SD<br />
• Akron, CO<br />
• Ord, NE<br />
• Rockport, MO<br />
• St. John, KS
Overview<br />
SDSU <strong>Management</strong> Software<br />
DSSAT Crop Model<br />
Challenges
SDSU <strong>Management</strong> Software<br />
Developed by Oswald (2006)<br />
Simulates a center pivot<br />
Field is divided in<strong>to</strong> 60 pie pieces<br />
• (soil water balance for each)<br />
Schedules irrigation based on ET<br />
calculation and soil water levels
SDSU <strong>Management</strong> Software<br />
Inputs include:<br />
• Crop type<br />
• Location<br />
• Weather data<br />
• <strong>Irrigation</strong> strategy<br />
Outputs include:<br />
• ET<br />
• <strong>Irrigation</strong> dates and amounts
<strong>Irrigation</strong> Strategy Defined<br />
<br />
e.g. MPAW = ( ( PAW2 – PAW1 ) / ( PM2 – PM1 ) ) * PM + PAW1
Spatial Variability in Soil <strong>Water</strong> Balance<br />
Brookings, SD, 1988, Corn, without ET Forecasting<br />
<strong>Water</strong> in Root Zone (cm)<br />
9<br />
8<br />
7<br />
6<br />
5<br />
4<br />
3<br />
2<br />
1<br />
0<br />
100 125 150 175 200 225 250<br />
Day of Year<br />
FC<br />
Maximum<br />
Mean + SDV<br />
Mean<br />
Mean - SDV<br />
Minimum<br />
MPAW
DSSAT Crop Model<br />
CERES-Maize model developed by<br />
Jones and Kiniry (1986)<br />
Daily calculation of:<br />
• ET and water uptake<br />
• <strong>Water</strong> and N stress<br />
• Corn development / dry matter<br />
accumulation
DSSAT Crop Model<br />
Inputs include:<br />
• Weather data<br />
• Soil data<br />
• Genetics (hybrid)<br />
• <strong>Management</strong> practices (e.g. irrigation)<br />
Outputs include:<br />
• Yield
Create Soil Profiles<br />
Brookings, SD, 1983-2006, Corn<br />
14000<br />
12000<br />
Mean Yield (kg/ha)<br />
10000<br />
8000<br />
6000<br />
4000<br />
2000<br />
0<br />
0 5 10 15 20<br />
Soil <strong>Water</strong> Holding Capacity (cm/m)
Compare Yield <strong>to</strong> County Averages<br />
Annual Irrigated Yield Comparison<br />
Brookings, SD<br />
Corn Yield (kg/ha)<br />
20000<br />
18000<br />
16000<br />
14000<br />
12000<br />
10000<br />
8000<br />
6000<br />
4000<br />
2000<br />
0<br />
1980 1985 1990 1995 2000 2005 2010<br />
Year<br />
DSSAT Optimum Yield<br />
County Average (irrigated)
Compare Yield <strong>to</strong> County Averages<br />
Corn Yield Without <strong>Irrigation</strong><br />
16000<br />
14000<br />
12000<br />
10000<br />
8000<br />
6000<br />
4000<br />
2000<br />
0<br />
DSSAT Best Variety<br />
Non-Irrigated County Avg.<br />
Oakes, ND<br />
Brookings, SD<br />
Akron, CO<br />
Ord, NE<br />
Rockport, MO<br />
St. John, KS<br />
Location<br />
Yield (kg/ha)
Test Yield Response <strong>to</strong> <strong>Irrigation</strong><br />
Yield v. <strong>Irrigation</strong><br />
16000<br />
14000<br />
Corn Yield (kg/ha)<br />
12000<br />
10000<br />
8000<br />
6000<br />
4000<br />
2000<br />
0<br />
0 10 20 30 40 50<br />
Seasonal <strong>Irrigation</strong> (cm)
Challenges<br />
DSSAT:<br />
• Lower ET<br />
<br />
(even though both used FAO-56)<br />
• Higher soil water balance<br />
• Less water stress on crops
ET Challenges<br />
<br />
Expected<br />
reference ET <strong>to</strong><br />
decrease with<br />
increasing dew<br />
point<br />
EO (mm)<br />
5.15<br />
5.14<br />
5.13<br />
5.12<br />
5.11<br />
5.1<br />
5.09<br />
5.08<br />
5.07<br />
5.06<br />
5.05<br />
DSSAT EO v. Tdew<br />
0 5 10 15 20 25 30<br />
Tdew (C)<br />
<br />
ET did not respond when RH values were<br />
changed <strong>to</strong> 0% and 100%
Crop ET: DSSAT Compared <strong>to</strong> SDSU<br />
<strong>Management</strong> Software<br />
Crop ET % Error<br />
Dry Year<br />
Normal Year<br />
Wet Year<br />
Akron, CO<br />
27.1*<br />
3.4*<br />
34.2<br />
Brookings, SD<br />
56.3<br />
10.8<br />
1.9<br />
Rockport, MO 47.6<br />
23.3<br />
*short season due <strong>to</strong> early crop failure in DSSAT<br />
22.7<br />
Reference ET error: 5 – 13%<br />
Some error in crop ET is due <strong>to</strong> crop coefficients (SDSU<br />
<strong>Management</strong> Software) and partitioning (DSSAT)
Comparison: Soil <strong>Water</strong> Levels<br />
Soil <strong>Water</strong> Balance, Brookings, SD, 1988<br />
63 L/s Pump (1000 GPM)<br />
Plant Available <strong>Water</strong> (%)<br />
100<br />
75<br />
50<br />
25<br />
0<br />
100 125 150 175 200 225 250<br />
Day of Year<br />
DSSAT<br />
SDSU<br />
<strong>Management</strong><br />
Software<br />
Minimum
Conclusion<br />
<br />
<br />
Challenges were encountered with the ET<br />
component in DSSAT.<br />
If differences in the water balance between<br />
DSSAT and SDSU <strong>Management</strong> Software can<br />
be reconciled, this may be a cost effective<br />
way <strong>to</strong> develop irrigation strategies for limited<br />
water scenarios.
Acknowledgement<br />
<br />
SD <strong>Water</strong> Resources Institute<br />
Reference<br />
<br />
Heeren, D.M., H.D. Werner, T.P. Trooien. 2007. Evaluation<br />
of irrigation strategies with the DSSAT cropping system<br />
model. ASABE Section Meeting Paper No. RRV-07132. St.<br />
Joseph, Mich.: ASABE.
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