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FIELD-SCALE N APPLICATION USING CROP REFLECTANCE SENSORS
Ken Sudduth and Newell KitchenUSDA-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
V7-V1030%
Adapted from Schepers et al., NE, U.S.A.
Application can be synchronized to time of maximum crop need
Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October
2012, Columbia, MO
0 - 910 - 1920 - 2930 - 3940 - 4950 - 5960 - 70
% of Years With Greater Than 14" Rainfall During April-June
Temporal variability in climate – crop – soil interaction
Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October
2012, Columbia, MO
Oran00 Rep1 Block6
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N rate (kg ha-1)
Yie
ld (M
g ha
-1) Nopt
Oran00 Rep3 Block26
0
4
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0 100 200 300
N rate (kg ha-1)
Yiel
d (M
g ha
-1) Nopt
Spatial variability in optimum N rate
32% of fields had within-field variation in EONR ≥ 100 lbs N/acre.
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-based N sensing
Passive (sunlight)crop sensors
Active light sourcecrop sensors
Remote sensing
Chlorophyll meter
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)
Crop Circle by Holland Scientific (now marketed by Ag Leader)
LED Light SourceDetectorSource Colimation
Source Optics
Detector Optics
24"
32"
DetectorColimation
GreenSeeker
Crop CircleACS-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
Collect Reference Data
Create whole-field reference map
Prior to Application
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
Collect Reference Data
Create whole-field reference map
Prior to Application
553600 553700
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Real-time sensing and control
Collect Reference Data
Create whole-field reference map
Get Current Position by GPS
Prior to Application
Get Reference Value at Current Point
Sensor 1 Sensor 2 Sensor 3 Sensor 4
Select and/or Combine Sensor Outputs
Spatial or time-base filtering
Real-time sensing and control
Collect Reference Data
Create whole-field reference map
Get Current Position by GPS
Prior to Application
Get Reference Value at Current Point
N Recommendation Algorithm
Smoothing, Deadband, Hysteresis
Valve Control Output
Application System
Select and/or Combine Sensor Outputs
Spatial or time-base filtering
Sensor 1 Sensor 2 Sensor 3 Sensor 4
So what about that algorithm?
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 sensorsCorn 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.
0.8 1.2 1.6 2 2.4R atio ta rget/Ratio re ference
40
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200
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Nra
te, l
bs N
/acr
e
C rop C ircle V6-V7G reenSeeker V6-V7C rop C ircle V8-V10G reenSeeker V8-V10
Missouri algorithm graphically
Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October
2012, Columbia, MO
Sensors+System+ Algorithm
Integrated systems are available
=Confusion?
Translating Missouri USDA-ARS Research and Technology into Practice A training session provided by USDA-ARS-CSWQRU, 10-11 October
2012, Columbia, MO
Anhydrous Ammonia
Fluid
Dry N Application Hardware
Anhydrous Ammonia
Dry N Application Hardware
Fluid
0.8 1.2 1.6 2 2.4Ratio ta rget/Ratio re ference
40
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160
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Nra
te, l
bs N
/acr
e
Crop C ircle V6-V7G reenSeeker V6-V7Crop C ircle V8-V10G reenSeeker V8-V10
However…Not all application hardware can accurately provide the ~ 4:1 range in rates needed
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
Fields following a droughty growing seasonTranslating 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