Sustainability & Energy Crops Peter Scharf University of Missouri Plant Sciences.
Missouri algorithm: Design & objectives Peter Scharf University of Missouri Peter Scharf Newell...
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![Page 1: Missouri algorithm: Design & objectives Peter Scharf University of Missouri Peter Scharf Newell Kitchen, Ken Sudduth, Glenn Davis, John Lory, Vicky Hubbard,](https://reader038.fdocuments.in/reader038/viewer/2022103007/56649f355503460f94c52b96/html5/thumbnails/1.jpg)
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
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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!!
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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
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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
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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
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Crop N need is variable: Crop N need is variable: MinnesotaMinnesota
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Overapplication = leftover N in soil
N underapplied N overapplied
Wasted $Environmental
risk
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Mouth of Mississippi RiverHuge algal
bloom
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Spatially intensive Spatially intensive diagnosis is neededdiagnosis is needed
How?How?
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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
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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
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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
![Page 13: Missouri algorithm: Design & objectives Peter Scharf University of Missouri Peter Scharf Newell Kitchen, Ken Sudduth, Glenn Davis, John Lory, Vicky Hubbard,](https://reader038.fdocuments.in/reader038/viewer/2022103007/56649f355503460f94c52b96/html5/thumbnails/13.jpg)
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
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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
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…or sidedressing solution
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…or with a high-clearance spinner
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…with a big sprayer
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…or a big injector
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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
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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
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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
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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
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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
![Page 24: Missouri algorithm: Design & objectives Peter Scharf University of Missouri Peter Scharf Newell Kitchen, Ken Sudduth, Glenn Davis, John Lory, Vicky Hubbard,](https://reader038.fdocuments.in/reader038/viewer/2022103007/56649f355503460f94c52b96/html5/thumbnails/24.jpg)
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
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Sensor Benefits:Sensor Benefits:
• Make sure enough N is appliedMake sure enough N is applied
• Avoid unneeded N applicationAvoid unneeded N application
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N application to head-high corn
N rate map
June 20, 2007
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129 bu/ac149 bu/ac
High-N reference area
115
175
175
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Sensor Benefits:Sensor Benefits:
• Make sure enough N is appliedMake sure enough N is applied
• Avoid unneeded N applicationAvoid unneeded N application
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August 1 Aerial Photo after the June 13 UAN Application
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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
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2008: Our first cotton demo
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