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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
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
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
0.15
0.20
0.25
0.30
0.10 0.15 0.20 0.25 0.30
GS Red/NIR ratio
CC
Am
ber
/NIR
ra
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
0.6
0.65
0.7
0.75
0.8
0.85
0.9
6:2
7
6:5
5
7:2
3
7:5
1
8:1
9
8:4
7
9:1
5
9:4
3
10
:11
10
:39
11
:07
11
:35
12
:03
12
:31
12
:59
13
:27
13
:55
14
:23
14
:51
15
:19
15
:47
16
:15
16
:43
17
:11
17
:39
18
:07
18
:35
19
:03
19
:31
19
:59
20
:41
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)
0
20
40
60
80
100
120
140
5:00 7:24 9:48 12:12 14:36 17:00 19:24
TIME
N R
AT
E
N RATE BASED ON VIS/NIR (REF= 8- 8:10)
0
20
40
60
80
100
120
140
5:00 7:24 9:48 12:12 14:36 17:00 19:24
TIME
N R
AT
E
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)
0
20
40
60
80
100
120
140
5:00 7:24 9:48 12:12 14:36 17:00 19:24
TIME
N R
AT
E
NRATE BASED ON VIS/NIR (REF= 8-8:10)
0
20
40
60
80
100
120
140
5:00 7:24 9:48 12:12 14:36 17:00 19:24
TIME
N R
AT
E