WITHIN-SEASON NITROGEN APPLICATION USING SENSORS

Ken Sudduth USDA-ARS Cropping Systems and Water Quality Research Unit Columbia, MO

Translating Missouri USDA-ARS Research and Technology into Practice

A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO

Variable-rate N: Why?

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Soil N supply is variable spatially and over time

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N is expensive

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N gets into water

Crop reflectance basics

 Stressed plants are slightly more reflective in the visible range and much less reflective in NIR than healthy plants.

Translating Missouri USDA-ARS Research and Technology into Practice

A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO

Crop reflectance as an indicator of plant health

 Usually reflectance data are used in a ratio  Removes ambient effects, including sensor height  Amplifies differences  Most common ratios:  Visible/NIR (inverse simple ratio, ISR)  NDVI (normalized difference vegetation index) NDVI = (NIR – visible)/(NIR + visible)

Translating Missouri USDA-ARS Research and Technology into Practice

A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO

NDVI 0.33 (June 15) NDVI 0.52 (July 15) NDVI 0.73 (Aug. 6)

Time series of NDVI maps from airborne images relates to corn yield

Translating Missouri USDA-ARS Research and Technology into Practice

A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO

Automating plant reflectance-based N sensing

Chlorophyll meter Passive (sunlight) crop sensors Remote sensing Active light source crop sensors

Translating Missouri USDA-ARS Research and Technology into Practice

A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO

Commercial active reflectance sensors

 Multiple reflectance sensor options for automated systems: • • • GreenSeeker ® Trimble Ag system distributed by OptRx ® Leader system distributed by Ag CropSpec ® system distributed by Topcon Precision Ag Adapted from John Shanahan, DuPont Pioneer

Translating Missouri USDA-ARS Research and Technology into Practice

A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO

Sensors on applicator for real-time control

Translating Missouri USDA-ARS Research and Technology into Practice

A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO

Standardizing sensor measurements

  Hybrid, crop age, etc. may indicate a different color/health relationship Standard approach has been to ratio measurements to those from a well-fertilized reference area within the field

Missouri algorithm for calculating N rate (lbs N/acre) from canopy sensors

Corn Growth Stage Sensor Type

V6-V7 (1 to 1.5-ft tall corn) V8-V10 (2 to 4-ft tall corn) Crop Circle

(330 x ISR target / ISR reference ) - 270 (250 x ISR target / ISR reference ) - 200

GreenSeeker

(220 x ISR target / ISR reference ) - 170 (170 x ISR target / ISR reference ) - 120

(see Agron. Tech. Note MO-35 for additional details)

 New approaches that eliminate the reference area need to be evaluated

Translating Missouri USDA-ARS Research and Technology into Practice

A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO

Non-N-limiting reference area

  Reflectance from a non-N-limiting reference strip or area is used to standardize the reflectance from the application area Requires N application to part of the field prior to sidedress

Translating Missouri USDA-ARS Research and Technology into Practice

A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO

Real-time sensing and control example

Prior to Application Sensor 1 Sensor 2 Sensor 3 Sensor 4 Collect Reference Data Select and/or Combine Sensor Outputs Create whole-field reference map Spatial or time-base filtering Get Reference Value at Current Point Get Current Position by GPS N Recommendation Algorithm Smoothing, Deadband, Hysteresis Valve Control Output Application System

Handheld commercial sensors

   Standard GreenSeeker and Crop Circle/OptRx  Requires external power supply, datalogger, and (if desired) GPS RapidSCAN by Holland Scientific   Includes GPS and datalogger internally Essentially handheld version of ACS 430/OptRX, with 3 measurement channels GreenSeeker handheld by Trimble   Provides an average NDVI for an area No GPS or datalogger, low cost

Translating Missouri USDA-ARS Research and Technology into Practice

A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO

Benefits of sensor-based VR nitrogen on corn

   Results of 55 on-farm studies across Missouri showed an average profit increase of $17/acre. On average, N use was reduced and yield slightly increased Variation in results shows potential for refined approach

Scharf, P.C., Shannon, D.K., Palm, H.L., Sudduth, K.A., Drummond, S.T., Kitchen, N.R., Mueller, L.J., Hubbard, V.C., and Oliveira, L.F. 2011. Sensor-based nitrogen application out-performed producer chosen rates for corn in on-farm demonstrations. Agronomy Journal 103(6):1683-1691. Translating Missouri USDA-ARS Research and Technology into Practice

A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO

Innovations in canopy sensing for N fertilization

         Choose between several visible wavebands to optimize sensitivity depending on crop or growth stage Consider different soil management zones or imagery zones with these proximal sensors for changing N rates Base N recommendation developed from yield maps Auto-calibrate sensors while driving to simplify operator involvement Robust algorithms that are sensitive to growth stage Substitute a virtual reference strip for a high-N reference Cut-back in N where yield remediation is unlikely Fuse other soil/plant sensors Package systems that link need assessment with delivery Adapted from John Shanahan, DuPont Pioneer

Translating Missouri USDA-ARS Research and Technology into Practice

A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO

Fields and situations most suited for sensor-based variable rate N application

       Fields with extreme variability in soil type Fields experiencing a wet spring or early summer (loss of applied N) and where additional N fertilizer is needed Fields that have received recent manure applications Fields receiving uneven N fertilization because of application equipment failure Fields coming out of pasture, hay, or CRP management Fields of corn-after-corn, particularly when the field has previously been cropped in a different rotation Fields following a droughty growing season

Translating Missouri USDA-ARS Research and Technology into Practice

A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO

Risks, concerns, and considerations

       Technical aptitude/ability Suitability of N application hardware Narrow window for application without high-clearance equipment Balance between meeting early-season N need and crop stress detection Suitability of a single reference for a large, variable field Algorithm?

How many, and which type of sensor?

Translating Missouri USDA-ARS Research and Technology into Practice

A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO