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Missouri algorithm for N in corn

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Presentation on theme: "Missouri algorithm for N in corn"— Presentation transcript:

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?

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