Yield Map Data Collection and Best Management Practices: New ...

Yield Map Data Collection and Best Management Practices: New ...

FIELD-SCALE N APPLICATION USING CROP REFLECTANCE SENSORS Ken Sudduth and Newell Kitchen USDA-ARS Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO Questions addressed in this presentation Why the reflectance sensor approach? How to implement it? What are some results from Missouri research? What are additional considerations?

Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO Why the reflectance sensor approach? Timing Temporal variability Spatial variability Automation Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO Application can be synchronized to

time of maximum crop need V7-V10 30% Adapted from Schepers et al., NE, U.S.A. Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO Temporal variability in climate crop soil interaction % of Years With Greater Than 14" Rainfall During April-June 0- 9 10 - 19 20 - 29 30 - 39 40 - 49 50 - 59 60 - 70 Translating Missouri USDA-ARS Research and Technology into Practice

A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO Spatial variability in optimum N rate Oran00 Rep1 Block6 Oran00 Rep3 Block26 16 16 12 12 8 32% of fields had withinfield variation in EONR 100 lbs N/acre. Nopt

Yield (Mg ha-1) Yield (Mg ha-1) 8 4 0 0 100 N rate (kg ha-1) 200 300 Nopt 4 0

0 100 N rate (kg ha -1) Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO 200 300 Automating plant-based N sensing Chlorophyll meter Passive (sunlight) crop sensors Active light source crop sensors Remote sensing

Implementing N sensing with active crop canopy reflectance sensors Sensors Real-time sensing and control system Algorithm Application hardware Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO Active reflectance sensors By using an internal light source, these sensors eliminate problems with sun angle and cloud

variations GreenSeeker by NTech Industries (now Trimble) LED Light Source Detector Source Colimation Detector Colimation Detector Optics Source Optics 32" Crop Circle by Holland Scientific (now marketed by Ag Leader)

24" GreenSeeker Crop Circle ACS-210 Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO Sensor outputs Raw reflectance data visible and NIR Ratio data Visible/NIR Vegetation index data, e.g. NDVI: NDVI = (NIR visible)/(NIR + visible) 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 Real-time sensing and control Prior to Application Collect Reference Data Create wholefield reference map 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

Prior to Application 4289950 4289900 Collect Reference Data 4289850 4289800 4289750 Create wholefield reference map 4289700 4289650

4289600 4289550 4289500 4289450 4289400 553600 553700 Real-time sensing and control Prior to Application Collect Reference Data Create wholefield reference map Get

Reference Value at Current Point Get Current Position by GPS Sensor 1 Sensor 2 Sensor 3 Select and/or Combine Sensor Outputs Spatial or time-base filtering Sensor 4 Real-time sensing and control

Prior to Application Collect Reference Data Create wholefield reference map Get Reference Value at Current Point Get Current Position by GPS Sensor 1 Sensor 2 Sensor 3

Sensor 4 Select and/or Combine Sensor Outputs Spatial or time-base filtering N Recommendatio n Algorithm So what about that algorithm? Smoothing, Deadband, Hysteresis Valve Control Output

Application System Algorithms, algorithms, and more algorithms. Research groups around the country have developed algorithms : Missouri Oklahoma Nebraska Virginia etc.

There is ongoing work to test these algorithms under a variety of conditions Can we get to a common algorithm? Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO Missouri algorithm developed from previous plot research Equations for calculating N rates (lbs N/acre) from active 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 ratiotarget / ratioreference) - 270

(250 x ratiotarget / ratioreference) - 200 GreenSeeker (220 x ratiotarget / ratioreference) - 170 (170 x ratiotarget / ratioreference) - 120 Notes: Maximum N rate should not exceed 220 lbs N/acre. For V6-V7 corn, the value of ratioreference should not exceed 0.37 for Crop Circle and 0.30 for GreeenSeeker. Set this as a ceiling. For V8-V10 corn, the value of ratioreference should not exceed

0.25 for Crop Circle and 0.18 for GreeenSeeker. Set this as a ceiling. 240 Missouri algorithm graphicall y N r a te , lb s N /a c r e 200 160 120 C G C G

80 r o p C ir c le V 6 - V re e n S e e k e r V 6 r o p C ir c le V 8 - V re e n S e e k e r V 8 7 -V 7 10 -V 1 0 40 0 .8 1 .2 1 .6 R a tio ta rg e t/ R a tio 2 re fe re n c e

Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO 2 .4 Sensors + System + Algorithm = Confusion? Integrated systems are available Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO Dry N Application Hardware Fluid

Anhydrous Ammonia Dry N Application Hardware 240 Fluid 200 N r a te , lb s N /a c r e However Not all application hardware can accurately provide the ~ 4:1 range in rates needed 160 120

C G C G 80 r o p C ir c le V 6 - V re e n S e e k e r V 6 r o p C ir c le V 8 - V re e n S e e k e r V 8 7 -V 7 10 -V 1 0 40 0 .8 Anhydrous Ammonia

1 .2 R a tio 1 .6 a t io ta rg e t/ R 2 re fe re n c e 2 .4 Commercial options are available 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 Translating Missouri USDA-ARS Research and Technology into Practice Fields following a droughty growing season 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

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