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

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

Sensor Algorithm Review Ph.D. Work of Fernando Solari

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

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

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

Sensitivity of Indices Variation in Canopy Greenness

Sensor-determined N Need Yield PLOT Yield REF If x<-9, RY=0.95-0.0018x-0.00000994x2 Else RY=0.95 R2=0.63*** NSUPPLIED=180-NESTIMATED-N IN-SEASON

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

2007 Research Sites

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)

At Planting N 40 172 80 210

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

40 40 210 80 250

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

Fields Grid Sampled

Other Spatial Soil Data Collected

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

Thank You!! Hopefully You Survived!!! Questions?