Optimizing Crop
Management
Practices with
DSSAT
Our Goal
• With increasing population and climate change,
the ability to maximize crop production is essential.
• We want to be able to predict optimal
management practices for a variety of situations,
including under environmental stresses such as
during a drought, while minimizing pollution from
unused fertilizer.
• We will use DSSAT to simulate crop growth under a
range of management practices and determine
the combination that produces the largest yield
with the smallest nitrogen pollution.
• Find areas of DSSAT that can be improved.
Sensitivity Analysis
• Variables examined:
o Days between irrigations
• 1 – 14 in increments of 1, 15 to 21 in increments of 3
o Total amount of water applied in irrigations throughout the growing
season
• 200 to 500 mm in increments of 10
o Number of applications of Nitrogen as fertilizer
• 0 to 3 in increments of 1
o Total amount of nitrogen applied throughout the growing season
• 50 to 290 kg/ha in increments of 10, 300 to 400 in increments of 50
o Number of applications of Phosphorus as fertilizer
• 0 to 2 in increments of 1
o Total amount of phosphorus applied throughout the growing season
• 5 to 20 kg/ha in increments of 5, 40 to 100 in increments of 20
Sensitivity Analysis
• Maize simulated in Ghana without precipitation
• Planting date: June 17, 2004
• Harvest date: September 6, 2004
Sensitivity Analysis
Sensitivity Analysis
Sensitivity Analysis
Sensitivity Analysis
Optimal conditions:
Harvest: 7860 kg/ha
Water amount: 320 mm
Days between irrigations: 5
N applied: 200 kg
N applications: 3
P applied: 80 kg
P applications: 2
Sensitivity Analysis
Under-watered conditions:
Harvest: 4160 kg/ha
Water amount: 200 mm
Days between irrigations: 5
N applied: 200 kg
N applications: 3
P applied: 80 kg
P applications: 2
Sensitivity Analysis
Overwatered conditions:
Harvest: 6375 kg/ha
Water amount: 500 mm
Days between irrigations: 5
N applied: 200 kg
N applications: 3
P applied: 80 kg
P applications: 2
Sensitivity Analysis
Infrequently watered
conditions:
Harvest: 6198 kg/ha
Water amount: 320 mm
Days between irrigations:
15
N applied: 200 kg
N applications: 3
P applied: 80 kg
P applications: 2
Sensitivity Analysis
Fertilizer deprived
conditions:
Harvest: 5918 kg/ha
Water amount: 320 mm
Days between irrigations: 5
N applied: 100 kg
N applications: 3
P applied: 40 kg
P applications: 2
Sensitivity Analysis
Under-watered conditions:
Harvest: 4160 kg/ha
Water amount: 200 mm
Days between irrigations: 5
N applied: 200 kg
N applications: 3
P applied: 80 kg
P applications: 2
Effects of Water Deficiency
Optimal Conditions
Under-watered Conditions
Effects of Water Deficiency
Optimal Conditions
Under-watered Conditions
Sensitivity Analysis
Overwatered conditions:
Harvest: 6375 kg/ha
Water amount: 500 mm
Days between irrigations: 5
N applied: 200 kg
N applications: 3
P applied: 80 kg
P applications: 2
Effects of Over-watering
Optimal Conditions
Over-watered Conditions
Effects of Water Deficiency
Optimal Conditions
Over-watered Conditions
Sensitivity Analysis
Infrequently watered
conditions:
Harvest: 6198 kg/ha
Water amount: 320 mm
Days between irrigations:
15
N applied: 200 kg
N applications: 3
P applied: 80 kg
P applications: 2
Infrequently
Optimal Conditions
Infrequently watered Conditions
Infrequently
Optimal Conditions
Infrequently watered Conditions
Sensitivity Analysis
Fertilizer deprived
conditions:
Harvest: 5918 kg/ha
Water amount: 320 mm
Days between irrigations: 5
N applied: 100 kg
N applications: 3
P applied: 40 kg
P applications: 2
Effects of Fertilizer Deficiency
Optimal Conditions
Fertilizer Deprived Conditions
Effects of Fertilizer Deficiency
Optimal Conditions
Fertilizer Deprived Conditions
LAI vs harvest
Days between irrigations
Linear fit: LAI = 0.85858 + 0.00034544*harvest
R squared value: 0.729
Unused nitrogen vs harvest
Unused nitrogen = nitrogen applied in fertilizer – cumulative nitrogen uptake
Conclusion
• Performed exhaustive sensitivity analysis across six
degrees of freedom. This can be used to help
identify optimal management practice strategies.
• These simulations and optimizations can be
reproduced with different crop types, weather
information, and soil properties.
• Can help identify weaknesses in DSSAT – for
example, LAI values seem to be off.
Mysteries of DSSAT
• Why does overwatering reduce
yield?
o Water pushes nutrients deeper into the soil faster
than roots can grow down?
• Why is there a spike in minimum
harvest weight when nitrogen is
added in two applications?
• Why is there a plateau in
cumulative nitrogen uptake?
o Crop doesn’t need more nitrogen in that growth
stage?
• Why does nitrogen spontaneously
appear in the top soil layer when water
deprived?
o Nitrogen from second layer is brought up along with
water?
• Why does an LAI of three seem to
be the maximum attainable value?
LAI vs harvest
Days between irrigations
Days between irrigations
Unused nitrogen vs harvest
Unused nitrogen = nitrogen applied in fertilizer – cumulative nitrogen uptake
Days between irrigations
Days between irrigations
1,5
Sensitivity Analysis
Sensitivity Analysis
Sensitivity Analysis