1. UNL Algorithm for N in Corn
J. Schepers, R. Ferguson, D. Francis, M. Coleman, D. Roberts, K. Holland, J. Shanahan

2. Variable N Rate Applicator
Active sensors
Active sensors
Pictured above is a prototype high-clearance systems with on-the-go active crop canopy sensors interfaced with a controller and drop nozzles, which are capable of entering cornfields during the V8 to R2 application window and apply N fluid fertilizer based on crop need. The system is designed to interface with a real-time-differential global positioning system (DGPS) .
Fertilizer Drops

3. Sensor Algorithm Review
Ph.D. Work of Fernando Solari

4. MSEA site, 10 years, 4 hybrids (Varvel et al., 2007)
180N
180N
2005 Experiment
~120N

5. 2005 Study At planting In- season 0N 45N 90N 270N 135N 180N V15 V11
Poner rayas celestito
V15
V11

6. Chlorophyll Meter, Sensor & Yield Data Collected
V11, V15, R2 & R4 Growth Stages
NDVI= (NIR - Amber)/(NIR + Amber)
Chlorophyll index = (NIR/Amber) - 1

7. Sensitivity of Indices Variation in Canopy Greenness

8. Sensor-determined N Need
Yield PLOT
Yield REF
If x<-9,
RY= x x2
Else
RY=0.95
R2=0.63***
NSUPPLIED=180-NESTIMATED-N IN-SEASON

9. On-farm Validation Work in 2007
Ph.D. Work of Darrin Roberts

10. 2007 Research Sites

11. Previous Research Shows
Avoiding early season N stress critical
This will likely require some preplant N
How much is enough?
Optimal N application window (V9-V15)

12. At Planting N 40
172 80
210

17. 40 lb preplant N was inadequate
July, 18 Image
High N Ref
2x Base + Sensor
UNL Algorithm
Base + Sensor
Base Only
40 lb preplant N was inadequate

18. 40 40
210 80
250

21. 40 lb preplant N was adequate
July, 18 Image
Base Only
Base Only
Base Only
40 lb preplant N was adequate

22. Fields Grid Sampled

23. Other Spatial Soil Data Collected

24. Possible systems With or without: GPS sensors +GPS
Regional satellite view
With or without:
GPS
sensors
One-Time or
SAM
+GPS
We envision a flexible management system for N that will enable producers to make intelligent decisions for corn production using a variety of inputs depending on the situation. At the broadest scale, producers could use some type of remote sensing (satellite or aircraft) imagery to provide large areas with a spatial resolution of 10 m. Individual field images could be examined for signs of spatial variability and delineated into management zones. The resulting management zone prescription map would serve as input to a variable-rate controller. Sometimes the spatial resolution, timeliness, or clarity of the imagery may not be adequate, in which case, the canopy sensor would serve as input into the variable rate controller. Finally, the on-the-go sensor could be integrated with a sensor adjustment map (SAM) whereby variable N applications are made using both sensor and MZ input.
Multiple Applications
-GPS

25. Thank You!! Hopefully You Survived!!!
Questions?