missouri algorithm for n in corn peter scharf, newell kitchen, and john lory university of missouri...

Missouri algorithm for N in corn

Peter Scharf, Newell Kitchen, and John Lory

University of Missouri and USDA-ARS

Missouri Algorithm Based on direct empirical relationship

between measured reflectance and measured optimal N rate Site characteristics

Very compatible with current sensor group approach We will likely use the algorithms that will be

developed from group activities

Missouri Algorithm Original calibration: Cropscan passive at V6

Green, Red edge, Blue-green best Green/Infrared best combination

Optimal N rate = 330 * (G/NIR)target/(G/NIR)high N – 270 Works with either 0 or 100 N applied preplant

Tentatively applied with Crop Circle active sensor Subsequent research agrees fairly well

Relationship between optimal N rate and sensor measurements

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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

Y = 330(X) – 270

Greenseeker Values swing more widely than Crop Circle

over the same range of corn N status Need equation with smaller slope

June 20 Ratio Comparison

y = 0.801x + 0.0723

R2 = 0.9450.10

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0.30

0.10 0.15 0.20 0.25 0.30

GS Red/NIR ratio

CC

Am

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/NIR

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tio

Growth stages Original calibration was for V6

Also use for V7 Chlorophyll meter, sensor research show that

slope decreases as season progresses Decreased slope to 3/4 for V8 to V10

Current Missouri Algorithms

SensorGrowth stage Equation

Crop Circle V6-V7 330 * (V/NIR)t/(V/NIR)hiN - 270

Crop Circle V8-V10 250 * (V/NIR)t/(V/NIR)hiN - 200

Greenseeker V6-V7 220 * (V/NIR)t/(V/NIR)hiN - 170

Greenseeker V8-V10 170 * (V/NIR)t/(V/NIR)hiN - 120

On-farm demos using Missouri algorithms

7 in 200412 in 200519 in 200628 in 2007

21 with USDA Spra-Coupe

35 with producer-owned applicators35 with producer-owned applicators

10 with retailer-owned applicators

Kansas producer 2006: 4000 Kansas producer 2006: 4000 acres of corn fertilized in six acres of corn fertilized in six days using high-clearance days using high-clearance spinner, sensors, & Missouri spinner, sensors, & Missouri algorithmalgorithm

On-farm demonstrations 32 on-farm demonstrations 2004-2006 with

producer rate & sensor variable-rate side-by-side and replicated

Average N savings = 31 lb N/acre Average yield loss = 1.7 bu/acre Yield & N economics

$2 to $10/ac benefit depending on prices used Doesn’t count technology & management costs

On-farm demonstrations Complication: sensor values change during

the day Probably mainly due to changes in:

Canopy architecture Internal leaf properties External leaf properties

Leaf wetness effect on sensor values

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0.65

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0.75

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0.85

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Time on 10 July 2006

ND

VI

40 inch

10 inch

20 inch

RainDew

RainDew

Why diurnal changes in sensor values?Leaf wetness is the only reason we’re

sure ofWet leaves are darkerNeed to re-measure high-N reference

when leaf wetness changesReference strips perpendicular to rows

can make this feasible

Reference stripsPerpendicular to rows?

Tried in on-farm demo in 2007 Real-time update of high-N reference

value Worked great

Apply with 4-wheeler + spinner?Aerial?

Diurnal changes: other impacts

We may consider changing to an algorithm based on NDVI Especially Greenseeker

Less sensitive to diurnal changes in sensor values

Diurnal sensitivity of N recs: Greenseeker/cotton example

N RATE BASED ON NDVI (REF= 8- 8:10)

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5:00 7:24 9:48 12:12 14:36 17:00 19:24

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N RATE BASED ON VIS/NIR (REF= 8- 8:10)

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5:00 7:24 9:48 12:12 14:36 17:00 19:24

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NDVI-based

VIS/NIR-based

Diurnal sensitivity of N recs: Crop Circle/cotton example

NDVI-based

VIS/NIR-based

NRATE BASED ON NDVI (REF= 8-8:10)

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5:00 7:24 9:48 12:12 14:36 17:00 19:24

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NRATE BASED ON VIS/NIR (REF= 8-8:10)

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5:00 7:24 9:48 12:12 14:36 17:00 19:24

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