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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0 100 200 300

N rate (kg ha-1)

Yie

ld (M

g ha

-1) Nopt

Oran00 Rep3 Block26

0

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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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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