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Missouri algorithm: Design & objectives. Peter Scharf University of Missouri. Peter Scharf Newell Kitchen, Ken Sudduth, Glenn Davis, John Lory, Vicky Hubbard, Kent Shannon, Harlan Palm. On the way here, I saw a lot of money laying on the ground!!. Missouri Algorithm: Objectives. - PowerPoint PPT Presentation
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Missouri algorithm:Missouri algorithm:Design & objectivesDesign & objectives
Peter ScharfPeter Scharf
University of MissouriUniversity of Missouri
Peter ScharfNewell Kitchen, Ken Sudduth, Glenn Davis, John Lory, Vicky Hubbard, Kent Shannon, Harlan Palm
On the way here,On the way here,I saw a lot of I saw a lot of
money laying on money laying on the ground!!the ground!!
Missouri Algorithm: Objectives
1. Don’t leave money laying on the ground
– Supply enough N to the crop to support full yield
– Don’t apply N that the crop doesn’t need
2. Don’t let N escape from fields to water
Crop N need is variableCrop N need is variable
• Twenty on-farm N rate experiments in Missouri, corn after soybean, no manure
• Most profitable N rates were 109, 114, 175, 0, 90, 190, 244, 63, 119, 300, 0, 146, 146, 180, 52, 175, 112, 149, 136, 114 lb N/acre
Crop N need is variable: Crop N need is variable: MissouriMissouri
Optim al N rates, kg/ha
0 to 80
80 to 120
120 to 160
160 to 200
200 to 280
Oran00 Rep3 Block26
0
4
8
12
16
0 100 200 300
N rate (kg ha-1)
Yie
ld (
Mg
ha-1
)
Nopt
Oran00 Rep3 Block26
0
4
8
12
16
0 100 200 300100 200 300
N rate (kg ha-1)
Yie
ld (
Mg
ha-1
)
Nopt
lb/ac
Crop N need is variable: Crop N need is variable: MinnesotaMinnesota
Overapplication = leftover N in soil
N underapplied N overapplied
Wasted $Environmental
risk
Mouth of Mississippi RiverHuge algal
bloom
Spatially intensive Spatially intensive diagnosis is neededdiagnosis is needed
How?How?
Diagnosing where to put more NDiagnosing where to put more N
PredictorPredictor % of variability in N % of variability in N need explainedneed explained
Yield 2 to 20
Soil nitrate 17 to 25
Soil N quick tests 0 to 18
Soil conductivity 8
Corn color 53 to 77
Missouri algorithm design:Missouri algorithm design:Just an empirical relationshipJust an empirical relationship
• John Lory and I: initial calibration with Cropscan
• Newell Kitchen et al: more recent field-scale calibration of Greenseeker and Crop Circle
• Multi-state (country) data from this group
0
50
100
150
200
250
0.9 1.1 1.3 1.5 1.7
Green/near infrared relative to high-N plots
Op
tim
um
sid
ed
ress
N ra
te
Missouri Algorithm: Objectives, Set 2
1. Deal with spatial variability in N need
2. Support producer, retailers, consultants in planned sidedress operations from V6 to V16
3. Support producer, retailers, consultants in rescue N applications when previously applied N has been lost
Supporting producers in planned sidedress operations using sensors
• 26 demo fields in 2007 ( )
• 61 demo fields 2004-2007
Nearly 30 demo fields 2008, including first cotton field
Color sensors can be used Color sensors can be used for sidedressing anhydrous…for sidedressing anhydrous…
sensorssensors
Computer in cab reads sensors, calculates N rate, directs controller
Controller runs ball valve to change fertilizer rate
…or sidedressing solution
…or with a high-clearance spinner
…with a big sprayer
…or a big injector
On-farm sensor demos 2004-2007On-farm sensor demos 2004-2007
N rate system
Average yield
Average N rate
Producer rate
157
Sensor-controlled
$ to sensor
On-farm sensor demos 2004-2007On-farm sensor demos 2004-2007
N rate system
Average yield
Average N rate
Producer rate
157
Sensor-controlled
156
$ to sensor
On-farm sensor demos 2004-2007On-farm sensor demos 2004-2007
N rate system
Average yield
Average N rate
Producer rate
157
Sensor-controlled
156
$ to sensor -$3
On-farm sensor demos 2004-2007On-farm sensor demos 2004-2007
N rate system
Average yield
Average N rate
Producer rate
157 145
Sensor-controlled
156
$ to sensor -$3
On-farm sensor demos 2004-2007On-farm sensor demos 2004-2007
N rate system
Average yield
Average N rate
Producer rate
157 145
Sensor-controlled
156 123
$ to sensor -$2
On-farm sensor demos 2004-2007On-farm sensor demos 2004-2007
N rate system
Average yield
Average N rate
Producer rate
157 145
Sensor-controlled
156 123
$ to sensor -$2 +$15
Overall:+$13/ac tosensors
Sensor Benefits:Sensor Benefits:
• Make sure enough N is appliedMake sure enough N is applied
• Avoid unneeded N applicationAvoid unneeded N application
N application to head-high corn
N rate map
June 20, 2007
129 bu/ac149 bu/ac
High-N reference area
115
175
175
Sensor Benefits:Sensor Benefits:
• Make sure enough N is appliedMake sure enough N is applied
• Avoid unneeded N applicationAvoid unneeded N application
August 1 Aerial Photo after the June 13 UAN Application
215.4 212.1 204.2 212.4 215.5 204.9 206.6
214.1 208.0 208.5 206.6 206.6 211.6 205.4
Variable
Fixed
Avg Bu/A
208.6
210.2
2008: Our first cotton demo
