Field-scale Nitrogen Application Using Crop Reflectance Sensors
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Transcript Field-scale Nitrogen Application Using Crop Reflectance Sensors
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
8
32% of fields had within-field
variation in EONR ≥ 100 lbs
N/acre.
Yield (Mg ha-1)
Yield (Mg ha-1)
Nopt
4
Nopt
4
0
0
0
100
N rate (kg ha-1)
200
300
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)
Crop Circle by Holland
Scientific (now marketed by
Ag Leader)
LED Light Source
Detector
Source Colimation
Detector
Colimation
Detector Optics
Source Optics
32"
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 whole-field
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 whole-field
reference map
4289700
4289650
4289600
4289550
4289500
4289450
4289400
553600
553700
Real-time sensing and control
Prior to Application
Collect
Reference
Data
Create whole-field
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 whole-field
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 Recommendation
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
graphically
Nrate, lbs N/acre
200
160
120
Crop Circle V6-V7
GreenSeeker V6-V7
Crop Circle V8-V10
GreenSeeker V8-V10
80
40
0.8
1.2
1.6
Ratiotarget/Ratioreference
Translating Missouri USDA-ARS Research and Technology into Practice
A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO
2
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
Nrate, lbs N/acre
However…
Not all application hardware can
accurately provide the ~ 4:1 range in
rates needed
160
120
Crop Circle V6-V7
GreenSeeker V6-V7
Crop Circle V8-V10
GreenSeeker V8-V10
80
40
0.8
Anhydrous Ammonia
1.2
1.6
Ratiotarget/Ratioreference
2
2.4
Commercial options are available
Fields and situations most suited for sensorbased 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 highclearance 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