1. Missouri algorithm for N in corn
Peter Scharf, Newell Kitchen, and John Lory
University of Missouri and USDA-ARS

2. 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

3. 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

4. Relationship between optimal N rate and sensor measurements
Y = 330(X) – 270

5. Greenseeker
Values swing more widely than Crop Circle over the same range of corn N status
Need equation with smaller slope

6. 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

7. Current Missouri Algorithms
Sensor
Growth stage
Equation
Crop Circle
V6-V7
330 * (V/NIR)t/(V/NIR)hiN - 270
V8-V10
250 * (V/NIR)t/(V/NIR)hiN - 200
Greenseeker
220 * (V/NIR)t/(V/NIR)hiN - 170
170 * (V/NIR)t/(V/NIR)hiN - 120

8. On-farm demos using Missouri algorithms

9. 21 with USDA Spra-Coupe

10. 35 with producer-owned applicators

11. 10 with retailer-owned applicators

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

13. On-farm demonstrations
32 on-farm demonstrations 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

14. On-farm demonstrations
Complication: sensor values change during the day
Probably mainly due to changes in:
Canopy architecture
Internal leaf properties
External leaf properties

15. Leaf wetness effect on sensor values
Dew
Rain

16. Why diurnal changes in sensor values?
Leaf wetness is the only reason we’re sure of
Wet leaves are darker
Need to re-measure high-N reference when leaf wetness changes
Reference strips perpendicular to rows can make this feasible

17. Reference strips Perpendicular 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?

18. Diurnal changes: other impacts
We may consider changing to an algorithm based on NDVI
Especially Greenseeker
Less sensitive to diurnal changes in sensor values

19. Diurnal sensitivity of N recs: Greenseeker/cotton example
NDVI-based
VIS/NIR-based

20. Diurnal sensitivity of N recs: Crop Circle/cotton example
NDVI-based
VIS/NIR-based

21. Thanks!!
Questions?
Comments?
Discussion?